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Aadhaar Bill 2016 Evaluated against the National Privacy Principles
http://editors.cis-india.org/internet-governance/aadhaar-bill-2016-evaluated-against-the-national-privacy-principles
<b>In this infographic, we evaluate the privacy provisions of the Aadhaar Bill 2016 against the national privacy principles developed by the Group of Experts on Privacy led by the Former Chief Justice A.P. Shah in 2012. The infographic is based on Vipul Kharbanda’s article 'Analysis of Aadhaar Act in the Context of A.P. Shah Committee Principles,' and is designed by Pooja Saxena, with inputs from Amber Sinha.</b>
<p> </p>
<h4>Download the infographic: <a href="https://github.com/cis-india/website/raw/master/infographics/CIS_Aadhaar-2016-Vs-Privacy-Principles_v.1.0.pdf">PDF</a> and <a href="https://github.com/cis-india/website/raw/master/infographics/CIS_Aadhaar-2016-Vs-Privacy-Principles_v.1.0.png">PNG</a>.</h4>
<p> </p>
<p><strong>License:</strong> It is shared under Creative Commons <a href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International</a> License.</p>
<p> </p>
<img src="https://github.com/cis-india/website/raw/master/infographics/CIS_Aadhaar-2016-Vs-Privacy-Principles_v.1.0.png" alt="Aadhaar Bill 2016 Evaluated against the National Privacy Principles" />
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/aadhaar-bill-2016-evaluated-against-the-national-privacy-principles'>http://editors.cis-india.org/internet-governance/aadhaar-bill-2016-evaluated-against-the-national-privacy-principles</a>
</p>
No publisherPooja Saxena and Amber SinhaUIDBig DataPrivacyInternet GovernanceInfographicDigital IndiaAadhaarBiometrics2016-03-21T08:38:34ZBlog EntryVulnerabilities in the UIDAI Implementation Not Addressed by the Aadhaar Bill, 2016
http://editors.cis-india.org/internet-governance/blog/vulnerabilities-in-the-uidai-implementation-not-addressed-by-the-aadhaar-bill-2016
<b>In this infographic, we document the various issues in the Aadhaar enrolment process implemented by the UIDAI, and highlight the vulnerabilities that the Aadhaar Bill, 2016 does not address. The infographic is based on Vidushi Marda’s article 'Data Flow in the Unique Identification Scheme of India,' and is designed by Pooja Saxena, with inputs from Amber Sinha.</b>
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<h4>Download the infographic: <a href="https://github.com/cis-india/website/raw/master/infographics/CIS_Aadhaar-2016-Enrolment-Vulnerabilities_v.1.0.pdf">PDF</a> and <a href="https://github.com/cis-india/website/raw/master/infographics/CIS_Aadhaar-2016-Enrolment-Vulnerabilities_v.1.0.png">PNG</a>.</h4>
<p> </p>
<p><strong>Credits:</strong> The illustration uses the following icons from The Noun Project - <a href="https://thenounproject.com/term/fingerprint/231547/">Thumpbrint</a> created by Daouna Jeong, Duplicate created by Pham Thi Dieu Linh, <a href="https://thenounproject.com/term/copy/377777/">Copy</a> created by Mahdi Ehsaei.</p>
<p><strong>License:</strong> It is shared under Creative Commons <a href="https://creativecommons.org/licenses/by/4.0/">Attribution 4.0 International</a> License.</p>
<p> </p>
<img src="https://github.com/cis-india/website/raw/master/infographics/CIS_Aadhaar-2016-Enrolment-Vulnerabilities_v.1.0.png" alt="Vulnerabilities in the UIDAI Implementation Not Addressed by the Aadhaar Bill, 2016" />
<p> </p>
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For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/vulnerabilities-in-the-uidai-implementation-not-addressed-by-the-aadhaar-bill-2016'>http://editors.cis-india.org/internet-governance/blog/vulnerabilities-in-the-uidai-implementation-not-addressed-by-the-aadhaar-bill-2016</a>
</p>
No publisherPooja Saxena and Amber SinhaUIDBig DataPrivacyInternet GovernanceInfographicDigital IndiaAadhaarBiometrics2016-03-21T08:33:53ZBlog EntryAdoption of Standards in Smart Cities - Way Forward for India
http://editors.cis-india.org/internet-governance/blog/adoption-of-standards-in-smart-cities-way-forward-for-india
<b>With a paradigm shift towards the concept of “Smart Cities’ globally, as well as India, such cities have been defined by several international standardization bodies and countries, however, there is no uniform definition adopted globally. The glue that allows infrastructures to link and operate efficiently is standards as they make technologies interoperable and efficient.</b>
<p style="text-align: justify; "><b><a href="http://editors.cis-india.org/internet-governance/blog/adoption-of-standards-in-smart-cities.pdf" class="internal-link">Click here to download the full file</a></b></p>
<p style="text-align: justify; ">Globally, the pace of urbanization is increasing exponentially. The world’s urban population is projected to rise from 3.6 billion to 6.3 billion between 2011 and 2050. A solution for the same has been development of sustainable cities by improving efficiency and integrating infrastructure and services <strong>[1]</strong>. It has been estimated that during the next 20 years, 30 Indians will leave rural India for urban areas every minute, necessitating smart and sustainable cities to accommodate them <strong>[2]</strong>. The Smart Cities Mission of the Ministry of Urban Development was announced in the year 2014, followed by selection of 100 cities in the year 2015 and 20 of them being selected for the first Phase of the project in the year 2016. The Mission <strong>[3]</strong> lists the “core infrastructural elements” that a smart city would incorporate like adequate water supply, assured electricity, sanitation, efficient public transport, affordable housing (especially for the poor), robust IT connectivity and digitisation, e-governance and citizen participation, sustainable environment, safety and security for citizens, health and education.</p>
<p style="text-align: justify; ">With a paradigm shift towards the concept of “Smart Cities’ globally, as well as India, such cities have been defined by several international standardization bodies and countries, however, there is no uniform definition adopted globally. The envisioned modern and smart city promises delivery of high quality services to the citizens and will harness data capture and communication management technologies. The performance of such cities would be monitored on the basis of physical as well as the social structure comprising of smart approaches and solution to utilities and transport.</p>
<p style="text-align: justify; ">The glue that allows infrastructures to link and operate efficiently is standards as they make technologies interoperable and efficient. Interoperability is essential and to ensure smart integration of various systems in a smart city, internationally agreed standards that include technical specifications and classifications must be adhered to. Development of international standards ensure seamless interaction between components from different suppliers and technologies <strong>[4]</strong>.</p>
<p style="text-align: justify; ">Standardized indicators within standards benefit smart cities in the following ways:</p>
<ol style="text-align: justify; ">
<li>
<div style="text-align: justify; ">Effective governance and efficient delivery of services.</div>
</li>
<li>
<div style="text-align: justify; ">International and Local targets, benchmarking and planning.</div>
</li>
<li>
<div style="text-align: justify; ">Informed decision making and policy formulation.</div>
</li>
<li>
<div style="text-align: justify; ">Leverage for funding and recognition in international entities.</div>
</li>
<li>
<div style="text-align: justify; ">Transparency and open data for investment attractiveness.</div>
</li>
<li>
<div style="text-align: justify; ">A reliable foundation for use of big data and the information explosion to assist cities in building core knowledge for city decision-making, and enable comparative insight.</div>
</li>
</ol>
<p style="text-align: justify; ">The adoption of standards for smart cities has been advocated across the world as they are perceived to be an effective tool to foster development of the cities. The Director of the ITU Telecommunication Standardization Bureau Chaesub Lee is of the view that “Smart cities will employ an abundance of technologies in the family of the Internet of Things (IoT) and standards will assist the harmonized implementation of IoT data and applications , contributing to effective horizontal integration of a city’s subsystems” <strong>[5]</strong>.</p>
<h3 style="text-align: justify; ">Smart Cities standards in India</h3>
<p style="text-align: justify; ">National Association of Software and Services Companies (NASSCOM) partnered with Accenture <strong>[6]</strong> to prepare a report called ‘Integrated ICT and Geospatial Technologies Framework for 100 Smart Cities Mission’ <strong>[7]</strong> to explore the role of ICT in developing smart cities <strong>[8]</strong>, after the announcement of the Mission by Indian Government. The report, released in May 2015, lists down 55 global standards, keeping in view several city sub-systems like urban planning, transport, governance, energy, climate and pollution management, etc which could be applicable to the smart cities in India.</p>
<p style="text-align: justify; ">Though NASSCOM is working closely with the Ministry of Urban Development to create a sustainable model for smart cities <strong>[9]</strong>, due to lack of regulatory standards for smart cities, the Bureau of Indian Standards (BIS) in India has undertaken the task to formulate standardised guidelines for central and state authorities in planning, design and construction of smart cities by setting up a technical committee under the Civil engineering department of the Bureau. However, adoption of the standards by implementing agencies would be voluntary and intends to complement internationally available documents in this area <strong>[10]</strong>.</p>
<p style="text-align: justify; ">Developing national standards in line with these international standards would enable interoperability (i.e. devices and systems working together) and provide a roadmap to address key issues like data protection, privacy and other inherent risks in the digital delivery and use of public services in the envisioned smart cities, which call for comprehensive data management standards in India to instill public confidence and trust <strong>[11]</strong>.</p>
<h3 style="text-align: justify; ">Key International Smart Cities Standards</h3>
<p style="text-align: justify; ">Following are the key internationally accepted and recognized Smart Cities standards developed by leading organisations and the national standardization bodies of several countries that India could adopt or develop national standards in line with these.</p>
<h4 style="text-align: justify; ">The International Organization for Standardization (ISO) - Smart Cities Standards</h4>
<p style="text-align: justify; ">ISO is an instrumental body advocating and developing for smart cities to safeguard rights of the people against a liveable and sustainable environment. The ISO Smart Cities Strategic Advisory Group uses the following working definition: A ‘Smart City’ is one that dramatically increases the pace at which it improves its social, economic and environmental (sustainability) outcomes, responding to challenges such as climate change, rapid population growth, and political and economic instability by fundamentally improving how it engages society, how it applies collaborative leadership methods, how it works across disciplines and city systems, and how it uses data information and modern technologies in order to transform services and quality of life for those in and involved with the city (residents, businesses, visitors), now and for the foreseeable future, without unfair disadvantage of others or degradation of the natural environment. [For details see ISO/TMB Smart Cities Strategic Advisory Group Final Report, September 2015 ( ISO Definition, June 2015)].</p>
<p style="text-align: justify; ">The ISO Technical Committee 268 works on standardization in the field of Sustainable Development in Communities <strong>[12]</strong> to encourage the development and implementation of holistic, cross-sector and area-based approaches to sustainable development in communities. The Committee comprises of 3 Working Groups <strong>[13]</strong>:</p>
<ul style="text-align: justify; ">
<li>
<div style="text-align: justify; ">Working Group 1: System Management ISO 37101- This standard sets requirements, guidance and supporting techniques for sustainable development in communities. It is designed to help all kinds of communities manage their sustainability, smartness and resilience to improve the contribution of communities to sustainable development and assess their performance in this area <strong>[14]</strong>.</div>
</li>
<li>
<div style="text-align: justify; ">Working Group 2 : City Indicators- The key Smart Cities Standards developed by ISO TC 268 WG 2 (City Indicators) are:</div>
</li>
</ul>
<h4 style="text-align: justify; ">ISO 37120 Sustainable Development of Communities — Indicators for City Services and Quality of Life</h4>
<p style="text-align: justify; ">One of the key standards and an important step in this regard was ISO 37120:2014 under the ISO’s Technical Committee 268 (See Working on Standardization in the field of Sustainable Development in Communities) providing clearly defined city performance indicators (divided into core and supporting indicators) as a benchmark for city services and quality of life, along with a standard approach for measuring each for city leaders and citizens <strong>[15]</strong>. The standard is global in scope and can help cities prioritize city budgets, improve operational transparency, support open data and applications <strong>[16]</strong>. It follows the principles <strong>[17]</strong> set out and can be used in conjunction with ISO 37101.</p>
<p style="text-align: justify; ">ISO 37120 was the first ISO Standard on Global City Indicators published in the year 2014, developed on the basis of a set of indicators developed and extensively tested by the Global City Indicators Facility (a project by University of Toronto) and its 250+ member cities globally. GCIF is committed to build standardized city indicators for performance management including a database of comparable statistics that allow cities to track their effectiveness on everything from planning and economic growth to transportation, safety and education <strong>[18]</strong>.</p>
<p style="text-align: justify; ">The World Council on City Data (WCCD) <strong>[19]</strong> - a sister organization of the GCI/GCIF - was established in the year 2014 to operationalize ISO 37120 across cities globally. The standards encompasses 100 indicators developed around 17 themes to support city services and quality of life, and is accessible through the WCCD Open City Data Portal which allows for cutting-edge visualizations and comparisons. Indian cities are not yet listed with WCCD <strong>[20]</strong>.</p>
<p style="text-align: justify; ">The indicators are listed under the following heads <strong>[21]</strong>:</p>
<ol style="text-align: justify; ">
<li>
<div style="text-align: justify; ">Economy</div>
</li>
<li>
<div style="text-align: justify; ">Education</div>
</li>
<li>
<div style="text-align: justify; ">Environment</div>
</li>
<li>
<div style="text-align: justify; ">Energy</div>
</li>
<li>
<div style="text-align: justify; ">Finance</div>
</li>
<li>
<div style="text-align: justify; ">Fire and Emergency Responses</div>
</li>
<li>
<div style="text-align: justify; ">Governance</div>
</li>
<li>
<div style="text-align: justify; ">Health</div>
</li>
<li>
<div style="text-align: justify; ">Safety</div>
</li>
<li>
<div style="text-align: justify; ">Shelter</div>
</li>
<li>
<div style="text-align: justify; ">Recreation</div>
</li>
<li>
<div style="text-align: justify; ">Solid Waste</div>
</li>
<li>
<div style="text-align: justify; ">Telecommunication and innovation</div>
</li>
<li>
<div style="text-align: justify; ">Transportation</div>
</li>
<li>
<div style="text-align: justify; ">Urban Planning</div>
</li>
<li>
<div style="text-align: justify; ">Waste water</div>
</li>
<li>
<div style="text-align: justify; ">Water and Sanitation</div>
</li>
</ol>
<p style="text-align: justify; ">This International Standard is applicable to any city, municipality or local government that undertakes to measure its performance in a comparable and verifiable manner, irrespective of size and location or level of development. City indicators have the potential to be used as critical tools for city managers, politicians, researchers, business leaders, planners, designers and other professionals <strong>[22]</strong>. The WCCD forum highlights need for cities to have a set of globally standardized indicators to <strong>[23]</strong>:</p>
<ol style="text-align: justify; ">
<li>
<div style="text-align: justify; ">Manage and make informed decisions through data analysis</div>
</li>
<li>
<div style="text-align: justify; ">Benchmark and target</div>
</li>
<li>
<div style="text-align: justify; ">Leverage Funding with senior levels of government</div>
</li>
<li>
<div style="text-align: justify; ">Plan and establish new frameworks for sustainable urban development</div>
</li>
<li>
<div style="text-align: justify; ">Evaluate the impact of infrastructure projects on the overall performance of a city.</div>
</li>
</ol>
<h4 style="text-align: justify; ">ISO/DTR 37121- Inventory and Review of Existing Indicators on Sustainable Development and Resilience in Cities</h4>
<p style="text-align: justify; ">The second standard under ISO TC 268 WG 2 is ISO 37121, which defines additional indicators related to sustainable development and resilience in cities. Some of the indicators include: Smart Cities, Smart Grid, Economic Resilience, Green Buildings, Political Resilience, Protection of biodiversity, etc. The complete list can be viewed on the Resilient Cities website <strong>[24]</strong>.</p>
<p style="text-align: justify; "><strong>Working Group 3:</strong> Terminology - There are no publicly available documents so far, giving details about the status of the activities of this group. The ISO Technical Committee 268 also includes Sub Committee 1 (Smart Community Infrastructure) <strong>[25]</strong>, comprising of the following Working Groups: 1) WG 1 Infrastructure metrics, and 2) WG 2 Smart Community Infrastructure.</p>
<p style="text-align: justify; ">The key Smart Cities Standards developed by ISO under this are:</p>
<ul style="text-align: justify; ">
<li>
<p style="text-align: justify; "><strong>ISO 37151:2015 Smart community infrastructures — Principles and Requirements for Performance Metrics</strong><br />In the year 2015, a new ISO technical specification for smart cities- 37151:2015 for Principles and requirements for performance metrics was released. The purpose of standardization in the field of smart community infrastructures such as energy, water, transportation, waste, information and communications technology (ICT), etc. is to promote the international trade of community infrastructure products and services and improve sustainability in communities by establishing harmonized product standards <strong>[26]</strong>. The metrics in this standard will support city and community managers in planning and measuring performance, and also compare and select procurement proposals for products and services geared at improving community infrastructures <strong>[27]</strong>. <br />This Technical Specification gives principles and specifies requirements for the definition,identification, optimization, and harmonization of community infrastructure performance metrics, and gives recommendations for analysis, regarding interoperability, safety, security of community infrastructures <strong>[28]</strong>. This new Technical Specification supports the use of the ISO 37120 <strong>[29]</strong>.</p>
</li>
<li>
<p style="text-align: justify; "><strong>ISO/TR 37150:2014 Smart Community Infrastructures - Review of Existing Activities Relevant to Metrics<br /></strong>This standard addresses community infrastructures such as energy, water, transportation, waste and information and communications technology (ICT). Smart community infrastructures take into consideration environmental impact, economic efficiency and quality of life by using information and communications technology (ICT) and renewable energies to achieve integrated management and optimized control of infrastructures. Integrating smart community infrastructures for a community helps improve the lifestyles of its citizens by, for example: reducing costs, increasing mobility and accessibility, and reducing environmental pollutants.<br />ISO/TR 37150 reviews relevant metrics for smart community infrastructures and provides stakeholders with a better understanding of the smart community infrastructures available around the world to help promote international trade of community infrastructure products and give information about leading-edge technologies to improve sustainability in communities <strong>[30]</strong>. This standard, along with the above mentioned standards <strong>[31]</strong> supports the multi-billion dollar smart cities technology industry.</p>
</li>
</ul>
<p style="text-align: justify; ">Several other ISO Working Groups developing standards applicable to smart and sustainable cities have been listed in our website <strong>[32]</strong>.</p>
<h4 style="text-align: justify; ">The International Telecommunications Union (ITU)</h4>
<p style="text-align: justify; ">The ITU is another global body working on development of standards regarding smart cities.</p>
<p style="text-align: justify; ">A study group was formed in the year 2015 to tackle standardization requirements for the Internet of Things, with an initial focus on IoT applications in smart cities to address urban development challenges <strong>[33]</strong>, to enable the coordinated development of IoT technologies, including machine-to-machine communications and ubiquitous sensor networks. The group is titled “ITU-T Study Group 20: IoT and its applications, including smart cities and communities”, established to develop standards that leverage IoT technologies to address urban-development challenges and the mechanisms for the interoperability of IoT applications and datasets employed by various vertically oriented industry sectors <strong>[34]</strong>.</p>
<p style="text-align: justify; ">ITU-T also concluded a focused study group looking at smart sustainable cities in May 2015, acting as an open platform for smart city stakeholders to exchange knowledge in the interests of identifying the standardized frameworks needed to support the integration of ICT services in smart cities. Its parent group is ITU-T Study Group 5, which has agreed on the following definition of a Smart Sustainable City:<br />"A smart sustainable city is an innovative city that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operation and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental as well as cultural aspects".</p>
<h4 style="text-align: justify; ">UK - British Standards Institution</h4>
<p style="text-align: justify; ">Apart from the global standards setting organisations, many countries have been looking at developing standards to address the growth of smart cities across the globe. In the UK, the British Standards Institution (BSI) has been commissioned by the UK Department of Business, Innovation and Skills (BIS) to conceive a Smart Cities Standards Strategy to identify vectors of smart city development where standards are needed. The standards would be developed through a consensus-driven process under the BSI to ensure good practise is shared between all the actors. The BIS launched the City's Standards Institute to bring together cities and key industry leaders and innovators to work together in identifying the challenges facing cities, providing solutions to common problems and defining the future of smart city standards <strong>[35]</strong>.</p>
<ul style="text-align: justify; ">
<li>
<p style="text-align: justify; "><strong>PAS 181</strong> <em><strong>Smart city framework- Guide to establishing strategies for smart cities and communities</strong></em> establishes a good practice framework for city leaders to develop, agree and deliver smart city strategies that can help transform their city’s ability to meet challenges faced in the future and meet the goals. The smart city framework (SCF) does not intend to describe a one-size-fits-all model for the future of UK cities but focuses on the enabling processes by which the innovative use of technology and data, together with organizational change, can help deliver the diverse visions for future UK cities in more efficient, effective and sustainable ways <strong>[36]</strong>.</p>
</li>
<li>
<p style="text-align: justify; "><strong>PD 8101</strong> <em><strong>Smart cities- Guide to the role of the planning and development process</strong></em><em> </em>gives guidance regarding planning for new development for smart city plans and<em> </em>provides an overview of the key issues to be considered and prioritized. The document is for use by local authority planning and regeneration officers to identify good practice in a UK context, and what tools they could use to implement this good practice. This aims to enable new developments to be built in a way that will support smart city aspirations at minimal cost <strong>[37]</strong>.</p>
</li>
<li>
<p style="text-align: justify; "><strong>PAS 182<em> Smart city concept model. Guide to establishing a model for data</em></strong><em> </em>establishes an interoperability framework and data-sharing between agencies for smart cities for the following purposes:</p>
<ol style="text-align: justify; ">
<li>To have a city where information can be shared and understood between organizations and people at each level</li>
<li>The derivation of data in each layer can be linked back to data in the previous layer </li>
<li>The impact of a decision can be observed back in operational data. The smart city concept model (SCCM) provides a framework that can normalize and classify information from many sources so that data sets can be discovered and combined to gain a better picture of the needs and behaviours of a city’s citizens (residents and businesses) to help identify issues and devise solutions. PAS 182 is aimed at organizations that provide services to communities in cities, and manage the resulting data, as well as decision-makers and policy developers in cities <strong>[38]</strong>.</li>
</ol> </li>
<li>
<p style="text-align: justify; "><strong>PAS 180 Smart cities <em>Vocabulary</em></strong> helps build a strong foundation for future standardization and good practices by providing an industry-agreed understanding of smart city terms and definitions to be used in the UK. It provides a working definition of a Smart City- “Smart Cities” is a term denoting the effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens <strong>[39]</strong>. This aims to help improve communication and understanding of smart cities by providing a common language for developers, designers, manufacturers and clients. The standard also defines smart city concepts across different infrastructure and systems’ elements used across all service delivery channels and is intended for city authorities and planners, buyers of smart city services and solutions <strong>[40]</strong>, as well as product and service providers.</p>
</li>
</ul>
<p style="text-align: justify; "> </p>
<h3 style="text-align: justify; ">Endnotes</h3>
<p style="text-align: justify; "><strong>[1]</strong> See: <a class="external-link" href="http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf">http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf</a>.</p>
<p style="text-align: justify; "><strong>[2]</strong> See: <a class="external-link" href="http://www.ibm.com/smarterplanet/in/en/sustainable_cities/ideas/">http://www.ibm.com/smarterplanet/in/en/sustainable_cities/ideas/</a>.</p>
<p style="text-align: justify; "><strong>[3]</strong> See: <a class="external-link" href="http://www.thehindubusinessline.com/economy/smart-cities-mission-welcome-to-tomorrows-world/article8163690.ece">http://www.thehindubusinessline.com/economy/smart-cities-mission-welcome-to-tomorrows-world/article8163690.ece</a>.</p>
<p style="text-align: justify; "><strong>[4]</strong> See: <a class="external-link" href="http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf">http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf</a>.</p>
<p style="text-align: justify; "><strong>[5]</strong> See: <a class="external-link" href="http://www.iso.org/iso/news.htm?refid=Ref2042">http://www.iso.org/iso/news.htm?refid=Ref2042</a>.</p>
<p style="text-align: justify; "><strong>[6]</strong> See: <a class="external-link" href="http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html">http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html</a>.</p>
<p style="text-align: justify; "><strong>[7]</strong> See: <a class="external-link" href="http://www.nasscom.in/integrated-ict-and-geospatial-technologies-framework-100-smart-cities-mission">http://www.nasscom.in/integrated-ict-and-geospatial-technologies-framework-100-smart-cities-mission</a>.</p>
<p style="text-align: justify; "><strong>[8]</strong> See: <a class="external-link" href="http://www.cxotoday.com/story/nasscom-creates-framework-for-smart-cities-project/">http://www.cxotoday.com/story/nasscom-creates-framework-for-smart-cities-project/</a>.</p>
<p style="text-align: justify; "><strong>[9]</strong> See: <a class="external-link" href="http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html">http://www.livemint.com/Companies/5Twmf8dUutLsJceegZ7I9K/Nasscom-partners-Accenture-to-form-ICT-framework-for-smart-c.html</a>.</p>
<p style="text-align: justify; "><strong>[10]</strong> See: <a class="external-link" href="http://www.business-standard.com/article/economy-policy/in-a-first-bis-to-come-up-with-standards-for-smart-cities-115060400931_1.html">http://www.business-standard.com/article/economy-policy/in-a-first-bis-to-come-up-with-standards-for-smart-cities-115060400931_1.html</a>.</p>
<p style="text-align: justify; "><strong>[11]</strong> See: <a class="external-link" href="http://www.longfinance.net/groups7/viewdiscussion/72-financing-financing-tomorrow-s-cities-how-standards-can-support-the-development-of-smart-cities.html?groupid=3">http://www.longfinance.net/groups7/viewdiscussion/72-financing-financing-tomorrow-s-cities-how-standards-can-support-the-development-of-smart-cities.html?groupid=3</a>.</p>
<p style="text-align: justify; "><strong>[12]</strong> See: <a class="external-link" href="http://www.iso.org/iso/iso_technical_committee?commid=656906">http://www.iso.org/iso/iso_technical_committee?commid=656906</a>.</p>
<p style="text-align: justify; "><strong>[13]</strong> See: <a class="external-link" href="http://cityminded.org/wp-content/uploads/2014/11/Patricia_McCarney_PDF.pdf">http://cityminded.org/wp-content/uploads/2014/11/Patricia_McCarney_PDF.pdf</a>.</p>
<p style="text-align: justify; "><strong>[14]</strong> See: <a class="external-link" href="http://www.iso.org/iso/news.htm?refid=Ref1877">http://www.iso.org/iso/news.htm?refid=Ref1877</a>.</p>
<p style="text-align: justify; "><strong>[15]</strong> See: <a class="external-link" href="http://smartcitiescouncil.com/article/new-iso-standard-gives-cities-common-performance-yardstick">http://smartcitiescouncil.com/article/new-iso-standard-gives-cities-common-performance-yardstick</a>.</p>
<p style="text-align: justify; "><strong>[16]</strong> See: <a class="external-link" href="http://smartcitiescouncil.com/article/dissecting-iso-37120-why-new-smart-city-standard-good-news-cities">http://smartcitiescouncil.com/article/dissecting-iso-37120-why-new-smart-city-standard-good-news-cities</a>.</p>
<p style="text-align: justify; "><strong>[17]</strong> See: <a class="external-link" href="http://www.iso.org/iso/catalogue_detail?csnumber=62436">http://www.iso.org/iso/catalogue_detail?csnumber=62436</a>.</p>
<p style="text-align: justify; "><strong>[18]</strong> See: <a class="external-link" href="http://www.cityindicators.org/">http://www.cityindicators.org/</a>.</p>
<p style="text-align: justify; "><strong>[19]</strong> See: <a class="external-link" href="http://www.dataforcities.org/">http://www.dataforcities.org/</a>.</p>
<p style="text-align: justify; "><strong>[20]</strong> See: <a class="external-link" href="http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html">http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html</a>.</p>
<p style="text-align: justify; "><strong>[21]</strong> See: <a class="external-link" href="http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html">http://news.dataforcities.org/2015/12/world-council-on-city-data-and-hatch.html</a>.</p>
<p style="text-align: justify; "><strong>[22]</strong> See: <a class="external-link" href="http://www.iso.org/iso/37120_briefing_note.pdf">http://www.iso.org/iso/37120_briefing_note.pdf</a>.</p>
<p style="text-align: justify; "><strong>[23]</strong> See: <a class="external-link" href="http://www.dataforcities.org/wccd/">http://www.dataforcities.org/wccd/</a>.</p>
<p style="text-align: justify; "><strong>[24]</strong> See: <a class="external-link" href="http://resilient-cities.iclei.org/fileadmin/sites/resilient-cities/files/Webinar_Series/HERNANDEZ_-_ICLEI_Resilient_Cities_Webinar__FINAL_.pdf">http://resilient-cities.iclei.org/fileadmin/sites/resilient-cities/files/Webinar_Series/HERNANDEZ_-_ICLEI_Resilient_Cities_Webinar__FINAL_.pdf</a>.</p>
<p style="text-align: justify; "><strong>[25]</strong> See: <a class="external-link" href="http://www.iso.org/iso/iso_technical_committee?commid=656967">http://www.iso.org/iso/iso_technical_committee?commid=656967</a>.</p>
<p style="text-align: justify; "><strong>[26]</strong> See: <a class="external-link" href="https://www.iso.org/obp/ui/#iso:std:iso:ts:37151:ed-1:v1:en">https://www.iso.org/obp/ui/#iso:std:iso:ts:37151:ed-1:v1:en</a>.</p>
<p style="text-align: justify; "><strong>[27]</strong> See: <a class="external-link" href="http://www.iso.org/iso/home/news_index/news_archive/news.htm?refid=Ref2001&utm_medium=email&utm_campaign=ISO+Newsletter+November&utm_content=ISO+Newsletter+November+CID_4182720c31ca2e71fa93d7c1f1e66e2f&utm_source=Email%20marketing%20software&utm_term=Read%20more">http://www.iso.org/iso/home/news_index/news_archive/news.htm?refid=Ref2001&utm_medium=email&utm_campaign=ISO+Newsletter+November&utm_content=ISO+Newsletter+November+CID_4182720c31ca2e71fa93d7c1f1e66e2f&utm_source=Email%20marketing%20software&utm_term=Read%20more</a>.</p>
<p style="text-align: justify; "><strong>[28]</strong> See: <a class="external-link" href="http://www.iso.org/iso/37120_briefing_note.pdf">http://www.iso.org/iso/37120_briefing_note.pdf</a>.</p>
<p style="text-align: justify; "><strong>[29]</strong> See: <a class="external-link" href="http://standardsforum.com/isots-37151-smart-cities-metrics/">http://standardsforum.com/isots-37151-smart-cities-metrics/</a>.</p>
<p style="text-align: justify; "><strong>[30]</strong> See: <a class="external-link" href="http://www.iso.org/iso/executive_summary_iso_37150.pdf">http://www.iso.org/iso/executive_summary_iso_37150.pdf</a>.</p>
<p style="text-align: justify; "><strong>[31]</strong> See: <a class="external-link" href="http://standardsforum.com/isots-37151-smart-cities-metrics/">http://standardsforum.com/isots-37151-smart-cities-metrics/</a>.</p>
<p style="text-align: justify; "><strong>[32]</strong> See: <a class="external-link" href="http://cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards">http://cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards</a>.</p>
<p style="text-align: justify; "><strong>[33]</strong> See: <a class="external-link" href="http://smartcitiescouncil.com/article/itu-takes-internet-things-standards-smart-cities">http://smartcitiescouncil.com/article/itu-takes-internet-things-standards-smart-cities</a>.</p>
<p style="text-align: justify; "><strong>[34]</strong> See: <a class="external-link" href="https://www.itu.int/net/pressoffice/press_releases/2015/22.aspx">https://www.itu.int/net/pressoffice/press_releases/2015/22.aspx</a>.</p>
<p style="text-align: justify; "><strong>[35]</strong> See: <a class="external-link" href="http://www.bsigroup.com/en-GB/smart-cities/">http://www.bsigroup.com/en-GB/smart-cities/</a>.</p>
<p style="text-align: justify; "><strong>[36]</strong> See: <a class="external-link" href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-181-smart-cities-framework/">http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-181-smart-cities-framework/</a>.</p>
<p style="text-align: justify; "><strong>[37]</strong> See: <a class="external-link" href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PD-8101-smart-cities-planning-guidelines/">http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PD-8101-smart-cities-planning-guidelines/</a>.</p>
<p style="text-align: justify; "><strong>[38]</strong> See: <a class="external-link" href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-182-smart-cities-data-concept-model/">http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-182-smart-cities-data-concept-model/</a>.</p>
<p style="text-align: justify; "><strong>[39]</strong> See: <a class="external-link" href="http://www.iso.org/iso/smart_cities_report-jtc1.pdf">http://www.iso.org/iso/smart_cities_report-jtc1.pdf</a>.</p>
<p style="text-align: justify; "><strong>[40]</strong> See: <a class="external-link" href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-180-smart-cities-terminology/">http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-180-smart-cities-terminology/</a>.</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/adoption-of-standards-in-smart-cities-way-forward-for-india'>http://editors.cis-india.org/internet-governance/blog/adoption-of-standards-in-smart-cities-way-forward-for-india</a>
</p>
No publishervanyaOpen StandardsBig DataOpen DataInternet GovernanceSmart Cities2016-04-11T03:04:46ZBlog EntryAnalysis of Aadhaar Act in the Context of A.P. Shah Committee Principles
http://editors.cis-india.org/internet-governance/blog/analysis-of-aadhaar-act-in-context-of-shah-committee-principles
<b>Whilst there are a number of controversies relating to the Aadhaar Act including the fact that it was introduced in a manner so as to circumvent the majority of the opposition in the upper house of the Parliament and that it was rushed through the Lok Sabha in a mere eight days, in this paper we shall discuss the substantial aspects of the Act in relation to privacy concerns which have been raised by a number of experts. In October 2012, the Group of Experts on Privacy constituted by the Planning Commission under the chairmanship of Justice AP Shah Committee submitted its report which listed nine principles of privacy which all legislations, especially those dealing with personal should adhere to. In this paper, we shall discuss how the Aadhaar Act fares vis-à-vis these nine principles.</b>
<p> </p>
<h2>Introduction</h2>
<p>The Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act, 2016 (the “Aadhaar Act”) was introduced in the Lok Sabha (lower house of the Parliament) by Minister of Finance, Mr. Arun Jaitley, in on March 3, 2016, and was passed by the Lok Sabha on March 11, 2016. It was sent back by the Rajya Sabha with suggestions but the Lok Sabha rejected those suggestions, which means that the Act is now deemed to have been passed by both houses as it was originally introduced as a Money Bill. Whilst there are a number of controversies relating to the Aadhaar Act including the fact that it was introduced in a manner so as to circumvent the majority of the opposition in the upper house of the Parliament and that it was rushed through the Lok Sabha in a mere eight days, in this paper we shall discuss the substantial aspects of the Act in relation to privacy concerns which have been raised by a number of experts. In October 2012, the Group of Experts on Privacy constituted by the Planning Commission under the chairmanship of Justice AP Shah Committee submitted its report which listed nine principles of privacy which all legislations, especially those dealing with personal should adhere to. In this paper, we shall discuss how the Aadhaar Act fares vis-à-vis these nine principles.</p>
<p>In order for the reader to better understand the frame of reference on which we shall analyse the Aadhaar Act, the nine principles contained in the report of the Group of Experts on Privacy are explained in brief below:</p>
<ul><li><strong>Principle 1: Notice</strong> - Does the legislation/regulation require that entities governed by the Act give simple to understand notice of its information practices to all individuals, in clear and concise language, before any personal information is collected from them.</li>
<li><strong>Principle 2: Choice and Consent</strong> - Does the legislation/regulation require that entities governed under the Act provide the individual with the option to opt in/opt out of providing their personal information.</li>
<li><strong>Principle 3: Collection Limitation</strong> - Does the legislation/regulation require that entities governed under the Act collect personal information from individuals only as is necessary for a purpose identified.</li>
<li><strong>Principle 4: Purpose Limitation</strong> - Does the legislation/regulation require that personal data collected and processed by entities governed by the Act be adequate and relevant to the purposes for which they are processed.</li>
<li><strong>Principle 5: Access and Correction</strong> - Does the legislation/regulation allow individuals: access to personal information about them held by an entity governed by the Act; the ability to seek correction, amendments, or deletion of such information where it is inaccurate, etc.</li>
<li><strong>Principle 6: Disclosure</strong> - Does the legislation ensure that information is only disclosed to third parties after notice and informed consent is obtained. Is disclosure allowed for law enforcement purposes done in accordance with laws in force.</li>
<li><strong>Principle 7: Security</strong> - Does the legislation/regulation ensure that information that is collected and processed under that Act, is done so in a manner that protects against loss, unauthorized access, destruction, etc.</li>
<li><strong>Principle 8: Openness</strong> - Does the legislation/regulation require that any entity processing data take all necessary steps to implement practices, procedures, policies and systems in a manner proportional to the scale, scope, and sensitivity to the data that is collected and processed and is this information made available to all individuals in an intelligible form, using clear and plain language?</li>
<li><strong>Principle 9: Accountability</strong> - Does the legislation/regulation provide for measures that ensure compliance of the privacy principles? This would include measures such as mechanisms to implement privacy policies; including tools, training, and education; and external and internal audits.</li></ul>
<p> </p>
<h2>Analysis of the Aadhaar Act</h2>
<p>The Aadhaar Act has been brought about to give legislative backing to the most ambitious individual identity programme in the world which aims to provide a unique identity number to the entire population of India. The rationale behind this scheme is to correctly identify the beneficiaries of government schemes and subsidies so that leakages in government subsidies may be reduced. In furtherance of this rationale the Aadhaar Act gives the Unique Identification Authority of India (“UIDAI”) the power to enroll individuals by collecting their demographic and biometric information and issuing an Aadhaar number to them. Below is an analysis of the Act based on the privacy principles enumerated I the A.P. Shah Committee Report.</p>
<h3>Collection Limitation</h3>
<p><strong>Collection of Biometric and Demographic Information:</strong> The Aadhaar Act entitles every “resident”
<strong>[1]</strong> to obtain an Aadhaar number by submitting his/her biometric (photograph, finger print, Iris scan) and demographic information (name, date of birth, address <strong>[2]</strong>) <strong>[3]</strong>. It must be noted that the Act leaves scope for further information to be included in the collection process if so specified by regulations. It must be noted that although the Act specifically provides what information can be collected, it does not specifically prohibit the collection of further information. This becomes relevant because it makes it possible for enrolling agencies to collect extra information relating to individuals without any legal implications of such act.</p>
<p><strong>Authentication Records:</strong> The UIDAI is mandated to maintain authentication records for a period which is yet to be specified (and shall be specified in the regulations) but it cannot collect or keep any information regarding the purpose for which the authentication request was made <strong>[4]</strong>.</p>
<p><strong>Unauthorized Collection:</strong> Any person who in not authorized to collect information under the Act, and pretends that he is authorized to do so, shall be punishable with imprisonment for a term which may extend to three years or with a fine which may extend to Rs. 10,000/- or both. In case of companies the maximum fine amount would be increased to Rs. 10,00,000/- <strong>[5]</strong>. It must be noted that the section, as it is currently worded seems to criminalize the act of impersonation of authorized individuals and the actual collection of information is not required to complete this offence. It is not clear if this section will apply if a person who is authorized to collect information under the Act in general, collects some information that he/she is not authorized to collect.</p>
<h3>Notice</h3>
<p><strong>Notice during Collection:</strong> The Aadhaar Act requires that the agencies enrolling people for distribution of Aadhaar numbers should give people notice regarding: (a) the manner in which the information shall be used; (b) the nature of recipients with whom the information is intended to be shared during authentication; and (c) the existence of a right to access information, the procedure for making requests for such access, and details of the person or department in-charge to whom such requests can be made <strong>[6]</strong>. A failure to comply with this requirement will make the agency liable for imprisonment of upto 3 years or a fine of Rs. 10,000/- or both. In case of companies the maximum fine amount would be increased to Rs. 10,00,000/- <strong>[7]</strong>. It must be noted that the Act leaves the manner of giving such notice in the realm of regulations and does not specify how this notice is to be provided, which leaves important specifics to the realm of the executive.</p>
<p><strong>Notice during Authentication:</strong> The Aadhaar Act requires that authenticating agencies shall give information to the individuals whose information is to be authenticated regarding (a) the nature of information that may be shared upon authentication; (b) the uses to which the information received during authentication may be put by the requesting entity; and (c) alternatives to submission of identity information to the requesting entity <strong>[8]</strong>. A failure to comply with this requirement will make the agency liable for imprisonment of upto 3 years or a fine of Rs. 10,000/- or both. In case of companies the maximum fine amount would be increased to Rs. 10,00,000/- <strong>[9]</strong>. Just as in the case of notice during collection, the manner in which the notice is required to be given is left to regulations leaving an unclear picture as to how comprehensive, accessible, and frequent this notice must be.</p>
<h3>Access and Correction</h3>
<p><strong>Updating Information:</strong> The Aadhaar Act give the UIDAI the power to require residents to update their demographic and biometric information from time to time so as to maintain its accuracy <strong>[10]</strong>.</p>
<p><strong>Access to Information:</strong> The Aadhaar Act provides that Aadhaar number holders may request the UIDAI to provide access to their identity information expect their core biometric information <strong>[11]</strong>. It is not clear why access to the core biometric information <strong>[12]</strong> is not provided to an individual. Further, since section 6 seems to place the responsibility of updation and accuracy of biometric information on the individual, it is not clear how a person is supposed to know that the biometric information contained in the database has changed if he/she does not have access to the same. It may also be noted that the Aadhaar Act provides only for a request to the UIDAI for access to the information and does not make access to the information a right of the individual, this would mean that it would be entirely upon the discretion of the UIDAI to refuse to grant access to the information once a request has been made.</p>
<p><strong>Alteration of Information:</strong> The Aadhaar Act gives individuals the right to request the UIDAI to alter their demographic if the same is incorrect or has changed and biometric information if it is lost or has changed. Upon receipt of such a request, if the UIDAI is satisfied, then it may make the necessary alteration and inform the individual accordingly. The Act also provides that no identity information in the Central database shall be altered except as provided in the regulations <strong>[13]</strong>. This section provides for alteration of identity information but only in the circumstances given in the section, for example demographic information cannot be changed if it has been lost, similarly biometric information cannot be changed if it is inaccurate. Further, the section does not give a right to the individual to get the information altered but only entitles him/her to request the UIDAI to make a change and the final decision is left to the “satisfaction” of the UIDAI.</p>
<p><strong>Access to Authentication Record:</strong> Every individual is given the right to obtain his/her authentication record in a manner to be specified by regulations. [14]</p>
<h3>Disclosure</h3>
<p><strong>Sharing during Authentication:</strong> The UIDAI is entitled to reply to any authentication query with a positive, negative or any other response which may be appropriate and may share identity information except core biometric information with the requesting entity <strong>[15]</strong>. The language in this provision is ambiguous and it is unclear what 'identity information' may be shared and why it would be necessary to share such information as Aadhaar is meant to be only a means of authentication so as to remove duplication.</p>
<p><strong>Potential Disclosure during Maintenance of CIDR:</strong> The UIDAI has been given the power to appoint any one or more entities to establish and maintain the Central Identities Data Repository (CIDR) <strong>[16]</strong>. If a private entity is involved in the maintenance and establishment of the CIDR it can be presumed that there is the possibilty that they would, to some degree, have access to the information stored in the CIDR, yet there are no clear standards in the Act regarding this potential access. And the process for appointing such entities. The fact that the UIDAI has been given the freedom to appoint an outside entity to maintain a sensitive asset such as the CIDR raises security concerns.</p>
<p><strong>Restriction on Sharing Information:</strong> The Aadhaar Act creates a blanket prohibition on the usage of core biometric information for any purpose other than generation of Aadhaar numbers and also prohibits its sharing for any reason whatsoever <strong>[17]</strong>. Other identity information is allowed to be shared in the manner specified under the Act or as may be specified in the regulations <strong>[18]</strong>. The Act further provides that the requesting entities shall not disclose the identity information except with the prior consent of the individual to whom the information relates <strong>[19]</strong>. There is also a prohibition on publicly displaying Aadhaar number or core biometric information except as specified by regulations <strong>[20]</strong>. Officers or the UIDAI or the employees of the agencies employed to maintain the CIDR are prohibited from revealing the information stored in the CIDR or authentication record to anyone <strong>[21]</strong>. It is not clear why an exception has been carved out and what circumstances would require publicly displaying Aadhaar numbers and core biometric information, especially since the reasons for which such important information may be displayed has been left up to regulations which have relatively less oversight. The section also provides the requesting entities with an option to further disclose information if they take consent of the individuals. This may lead to a situation where a requesting entity, perhaps the of an essential service, may take the consent of the individual to disclose his/her information in a standard form contract, without the option of saying no to such a request. It may lead to situations where the option is between giving consent to disclosure or denial or service altogether. For this reason it is necessary that there should be an opt in and opt out provision wherever a requesting entity has the power to ask for disclosure of information, so that people are not coerced into giving consent.</p>
<p><strong>Disclosure in Specific Cases:</strong> The prohibition on disclosure of information (except for core biometric information) does not apply in case of any disclosure made pursuant to an order of a court not below that of a District Judge <strong>[22]</strong>. There is another exception to the prohibition on disclosure of information (including core biometric information) in the interest of national security if so directed by an officer not below the rank of a Joint Secretary to the Government of India specially authorised in this behalf by an order of the Central Government. Before any such direction can take effect, it will be reviewed by an oversight committee consisting of the Cabinet Secretary and the Secretaries to the Government of India in the Department of Legal Affairs and the Department of Electronics and Information Technology. Any such direction shall be valid for a period of three months and may be extended by another three months after the review by the Oversight Committee <strong>[23]</strong>. Although this provision has been criticized, and rightly so, for the lack of accountability since the entire process is being handled within the executive and there is no independent oversight, however it must be mentioned that the level of oversight provided here is similar to that provided to interception requests, which involve a much graver if not the same level of invasion of privacy.</p>
<p><strong>Penalty for Disclosure:</strong> Any person who intentionally and in an unauthorized manner discloses, transmits, copies or otherwise disseminates any identity information collected in the course of enrolment or authentication shall be punishable with imprisonment of upto 3 years or a fine of Rs. 10,000/- or both. In case of companies the maximum fine amount would be increased to Rs. 10,00,000/ <strong>[24]</strong>. Further any person who intentionally and in an unathorised manner, accesses information in the CIDR <strong>[25]</strong>, downloads, copies or extracts any data from the CIDR <strong>[26]</strong>, or reveals or shares or distributes any identity information, shall be punishable with imprisonment of upto 3 years and a fine of not less than Rs. 10,00,000/-.</p>
<h3>Consent</h3>
<p><strong>Consent for Authentication:</strong> A requesting entity has to take the consent of the individual before collecting his/her identity information for the purposes of authentication and also has to inform the individual of the alternatives to submission of the identity information <strong>[27]</strong>. Although this provision requires entities to take consent from the individuals before collecting information for authentication, however how useful this requirement of consent would be, still remains to be seen. There may be instances where a requesting entity may take the consent of the individual in a standard form contract, without the individual realizing what he/she is consenting to.</p>
<p><strong>Note:</strong> The Aadhaar Act provides no requirement or standard for the form of consent that must be taken during enrollment. This is significant as it is the point at which individuals are providing raw biometric material and during previous enrollment, has been a point of weakness as the consent taken is an enabler to function creep as it allows the UIDAI to share information with engaged in delivery of welfare services <strong>[28]</strong>.</p>
<h3>Purpose</h3>
<p><strong>Use of Information:</strong> The authenticating entities are allowed to use the identity information only for the purpose of submission to the CIDR for authentication <strong>[29]</strong>. Further, the Act specifies that identity information available with a requesting entity shall not be used for any purpose other than that specified to the individual at the time of submitting the information for authentication <strong>[30]</strong>. The Act also provides that any authentication entity which uses the information for any purpose not already specified will be liable to punishment of imprisonment of upto 3 years or a fine of Rs. 10,000/- or both. In case of companies the maximum fine amount would be increased to Rs. 10,00,000/ <strong>[31]</strong>.</p>
<h3>Security</h3>
<p><strong>Security and Confidentiality of Information:</strong> It is the responsibility of the UIDAI to ensure the security and confidentiality of the identity and authentication information and it is required to take all necessary action to ensure that the information in the CIDR is protected against unauthorized access, use or disclosure and against accidental or intentional destruction, loss or damage <strong>[32]</strong>. The UIDAI is required to adopt and implement appropriate technical and organisational security measures and also ensure that its contractors do the same <strong>[33]</strong>. It is also required to ensure that the agreements entered into with its contractors impose the same conditions as are imposed on the UIDAI under the Act and that they shall act only upon the instructions of the UIDAI <strong>[34]</strong>.</p>
<p><strong>Biometric Information to be Electronic Record:</strong> The biometric information collected by the UIDAI has been deemed to be an “electronic record” as well as “sensitive personal data or information”, which would mean that in addition to the provisions of the Aadhaar Act, the provisions contained in the Information Technology Act, 2000 will also apply to such information <strong>[35]</strong>. It must be noted that while the Act lays down the principle that UIDAI is required to ensure the saecurity of the information, it does not lay down any guidelines as to the minimum security standards to be implemented by the Authority. However, through this section the legislature has linked the security standards contained in the IT Act to the information contained in this Act. While this is a clean way of dealing with the issue, some people may argue that the extremely sensitive nature of the information contained in the CIDR requires the standards for security to be much stricter than those provided in the IT Act. However, a perusal of Rule 8 of the Information Technology (Reasonable security practices and procedures and sensitive personal data or information) Rules, 2011 shows that the Rules themselves provide that the standard of security must be commensurate with the information assets being protected. It would thus seem that the Act provides enough room to protect such important information, but perhaps leaves too much room for interpretation for such an important issue.</p>
<p><strong>Penalty for Unauthorised Access:</strong> Apart from the security provisions included in the legislation, the Aadhaar Act also provides for punishment of imprisonment of upto 3 years and a fine which shall not be less than Rs. 10,00,000/-, in case of the following offences:</p>
<ol><li>introduction of any virus or other computer contaminant in the CIDR <strong>[36]</strong>;</li>
<li>causing damage to the data in the CIDR <strong>[37]</strong>;</li>
<li>disruption of access to the CIDR <strong>[38]</strong>;</li>
<li>denial of access to any person who is authorised to access the CIDR <strong>[39]</strong>;</li>
<li>destruction, deletion or alteration of any information stored in any removable storage media or in the CIDR or diminishing its value or utility or affecting it injuriously by any means <strong>[40]</strong>;</li>
<li>stealing, concealing, destroying or altering any computer source code used by the Authority with an intention to cause damage <strong>[41]</strong>.</li></ol>
<p>Further, unauthorized usage or tampering with the data in the CIDR or in any removable storage medium with the intent of modifying information relating to Aadhaar number holder or discovering any information thereof, is also punishable with imprisonment for a term which may extend to 3 years and also a fine which may extend to Rs. 10,000/- <strong>[42]</strong>.</p>
<h3>Accountability</h3>
<p><strong>Inspections and Audits:</strong> One of the functions listed in the powers and functions of the UIDAI is the power to call for information and records, conduct inspections, inquiries and audit of the operations of the CIDR, Registrars, enrolling agencies and other agencies appointed under the Aadhaar Act <strong>[43]</strong>.</p>
<p><strong>Grievance Redressal:</strong> Another function of the UIDAI is to set up facilitation centres and grievance redressal mechanisms for redressal of grievances of individuals, Registrars, enrolling agencies and other service providers <strong>[44]</strong>. It must be said here that considering the importance that the government has given to and intends to give to Aadhaar in the future, an essential task such as grievance redressal should not be left entirely to the discretion of the UIDAI and some grievance redressal mechanism should be incorporated into the Act itself.</p>
<h3>Openness</h3>
<p>There does not seem to be any provision in the Aadhaar Act which requires the UIDAI to make its privacy policies and procedure available to the public in general even though the UIDAI has the responsibility to maintain the security and confidentiality of the information.</p>
<p> </p>
<h2>Endnotes</h2>
<p><strong>[1]</strong> A resident is defined as any person who has resided in India for a period of atleasy 182 days in the previous 12 months.</p>
<p><strong>[2]</strong> It has been specified that demographic information will not include race, religion, caste, tribe, ethnicity, language, records of entitlement, income or medical history.</p>
<p><strong>[3]</strong> Section 3(1) of the Aadhaar Act.</p>
<p><strong>[4]</strong> Section 32(1) and 32(3) of the Aadhaar Act.</p>
<p><strong>[5]</strong> Section 36 of the Aadhaar Act.</p>
<p><strong>[6]</strong> Section 3(2) of the Aadhaar Act.</p>
<p><strong>[7]</strong> Section 41 of the Aadhaar Act.</p>
<p><strong>[8]</strong> Section 8(3) of the Aadhaar Act.</p>
<p><strong>[9]</strong> Section 41 of the Aadhaar Act.</p>
<p><strong>[10]</strong> Section 6 of the Aadhaar Act.</p>
<p><strong>[11]</strong> Section 28, <em>proviso</em> of the Aadhaar Act.</p>
<p><strong>[12]</strong> Core biometric information is defined as fingerprints, iris scan or other biological attributes which may be specified by regulations.</p>
<p><strong>[13]</strong> Section 31 of the Aadhaar Act.</p>
<p><strong>[14]</strong> Section 32(2) of the Aadhaar Act.</p>
<p><strong>[15]</strong> Section 8(4) of the Aadhaar Act.</p>
<p><strong>[16]</strong> Section 10 of the Aadhaar Act.</p>
<p><strong>[17]</strong> Section 29(1) of the Aadhaar Act.</p>
<p><strong>[18]</strong> Section 29(2) of the Aadhaar Act.</p>
<p><strong>[19]</strong> Section 29(3)(b) of the Aadhaar Act.</p>
<p><strong>[20]</strong> Section 29(4) of the Aadhaar Act.</p>
<p><strong>[21]</strong> Section 28(5) of the Aadhaar Act.</p>
<p><strong>[22]</strong> Section 33(1) of the Aadhaar Act.</p>
<p><strong>[23]</strong> Section 33(2) of the Aadhaar Act.</p>
<p><strong>[24]</strong> Section 37 of the Aadhaar Act.</p>
<p><strong>[25]</strong> Section 38(a) of the Aadhaar Act.</p>
<p><strong>[26]</strong> Section 38(b) of the Aadhaar Act.</p>
<p><strong>[27]</strong> Section 8(2)(a) and (c) of the Aadhaar Act.</p>
<p><strong>[28]</strong> For example, see: <a href="http://www.karnataka.gov.in/aadhaar/Downloads/Application%20form%20-%20English.pdf">http://www.karnataka.gov.in/aadhaar/Downloads /Application%20form%20-%20English.pdf</a>.</p>
<p><strong>[29]</strong> Section 8(2)(b) of the Aadhaar Act.</p>
<p><strong>[30]</strong> Section 29(3)(a) of the Aadhaar Act.</p>
<p><strong>[31]</strong> Section 37 of the Aadhaar Act.</p>
<p><strong>[32]</strong> Section 28(1), (2) and (3) of the Aadhaar Act.</p>
<p><strong>[33]</strong> Section 28(4)(a) and (b) of the Aadhaar Act.</p>
<p><strong>[34]</strong> Section 28(4)(c) of the Aadhaar Act.</p>
<p><strong>[35]</strong> Section 30 of the Aadhaar Act.</p>
<p><strong>[36]</strong> Section 38(c) of the Aadhaar Act.</p>
<p><strong>[37]</strong> Section 38(d) of the Aadhaar Act.</p>
<p><strong>[38]</strong> Section 38(e) of the Aadhaar Act.</p>
<p><strong>[39]</strong> Section 38(f) of the Aadhaar Act.</p>
<p><strong>[40]</strong> Section 38(h) of the Aadhaar Act.</p>
<p><strong>[41]</strong> Section 38(i) of the Aadhaar Act.</p>
<p><strong>[42]</strong> Section 39 of the Aadhaar Act.</p>
<p><strong>[43]</strong> Section 23(2)(l) of the Aadhaar Act.</p>
<p><strong>[44]</strong> Section 23(2)(s) of the Aadhaar Act.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/analysis-of-aadhaar-act-in-context-of-shah-committee-principles'>http://editors.cis-india.org/internet-governance/blog/analysis-of-aadhaar-act-in-context-of-shah-committee-principles</a>
</p>
No publisherVipul KharbandaBig DataPrivacyInternet GovernanceFeaturedDigital IndiaAadhaarBiometricsHomepage2016-03-17T19:43:53ZBlog EntryList of Recommendations on the Aadhaar Bill, 2016 - Letter Submitted to the Members of Parliament
http://editors.cis-india.org/internet-governance/blog/list-of-recommendations-on-the-aadhaar-bill-2016
<b>On Friday, March 11, the Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016. The Bill was introduced as a money bill and there was no public consultation to evaluate the provisions therein even though there are very serious ramifications for the Right to Privacy and the Right to Association and
Assembly. Based on these concerns, and numerous others, we submitted an initial list of recommendations to the Members of Parliaments to highlight the aspects of the Bill that require immediate attention.</b>
<p> </p>
<h4>Download the submission letter: <a href="https://github.com/cis-india/website/raw/master/docs/CIS_Aadhaar-Bill-2016_List-of-Recommendations_2016.03.16.pdf">PDF</a>.</h4>
<p> </p>
<h3>Text of the Submission</h3>
<p>On Friday, March 11, the Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016. The Bill was introduced as a money bill and there was no public consultation to evaluate the provisions therein even though there are very serious ramifications for the Right to Privacy and the Right to Association and Assembly. The Bill has made it compulsory for all Indian to enroll for Aadhaar in order to receive any subsidy, benefit, or service from the Government whose expenditure is incurred from the Consolidate Fund of India. Apart from the issue of centralisation of the national biometric database leading to a deep national vulnerability, the Bill also keeps unaddressed two serious concerns regarding the technological framework concerned:</p>
<ul><li><strong>Identification without Consent:</strong> Before the Aadhaar project it was not possible for the Indian government or any private entity to identify citizens (and all residents) without their consent. But biometrics allow for non-consensual and covert identification and authentication. The only way to fix this is to change the technology configuration and architecture of the project. The law cannot be used to correct the problems in the technological design of the project.<br /><br /></li>
<li><strong>Fallible Technology:</strong> The Biometrics Standards Committee of UIDAI has acknowledged the lack of data on how a biometric authentication technology will scale up where the population is about 1.2 billion. The technology has been tested and found feasible only for a population of 200 million. Further, a report by 4G Identity Solutions estimates that while in any population, approximately 5% of the people have unreadable fingerprints, in India it could lead to a failure to enroll up to 15% of the population. For the current Indian population of 1.2 billion the expected proportion of duplicates is 1/121, a ratio which is far too high. <strong>[1]</strong></li></ul>
<p>Based on these concerns, and numerous others, we sincerely request you to ensure that the Bill is rigorously discussed in Rajya Sabha, in public, and, if needed, also by a Parliamentary Standing Committee, before considering its approval and implementation. Towards this, we humbly submit an initial list of recommendations to highlight the aspects of the Bill that require immediate attention:</p>
<ol><li><strong>Implement the Recommendations of the Shah and Sinha Committees:</strong> The report by the Group of Experts on Privacy chaired by the Former Chief Justice A P Shah <strong>[2]</strong> and the report by the Parliamentary Standing Committee on Finance (2011-2012) chaired by Shri Yashwant Sinha <strong>[3]</strong> have suggested a rigorous and extensive range of recommendations on the Aadhaar / UIDAI / NIAI project and the National Identification Authority of India Bill, 2010 from which the majority sections of the Aadhaar Bill, 2016, are drawn. We request that these recommendations are seriously considered and incorporated into the Aadhaar Bill, 2016.<br /><br /></li>
<li><strong>Authentication using the Aadhaar number for receiving government subsidies, benefits, and services cannot be made mandatory:</strong> Section 7 of the Aadhaar Bill, 2016, states that authentication of the person using her/his Aadhaar number can be made mandatory for the purpose of disbursement of government subsidies, benefits, and services; and in case the person does not have an Aadhaar number, s/he will have to apply for Aadhaar enrolment. This sharply contradicts the claims made by UIDAI earlier that the Aadhaar number is “optional, and not mandatory”, and more importantly the directive given by the Supreme Court (via order dated August 11, 2015). The Bill must explicitly state that the Aadhaar number is only optional, and not mandatory, and a person without an Aadhaar number cannot be denied any democratic rights, and public subsidies, benefits, and services, and any private services.<br /><br /></li>
<li><strong>Vulnerabilities in the Enrolment Process:</strong> The Bill does not address already documented issues in the enrolment process. In the absence of an exhaustive list of information to be collected, some Registrars are permitted to collect extra and unnecessary information. Also, storage of data for elongated periods with Enrollment agencies creates security risks. These vulnerabilities need to be prevented through specific provisions. It should also be mandated for all entities including the Enrolment Agencies, Registrars, CIDR and the requesting entities to shift to secure system like PKI based cryptography to ensure secure method of data transfer.<br /><br /></li>
<li><strong>Precisely Define and Provide Legal Framework for Collection and Sharing of Biometric Data of Citizens:</strong> The Bill defines “biometric information” is defined to include within its scope “photograph, fingerprint, iris scan, or other such biological attributes of an individual.” This definition gives broad and sweeping discretionary power to the UIDAI / Central Government to increase the scope of the term. The definition should be exhaustive in its scope so that a legislative act is required to modify it in any way.<br /><br /></li>
<li><strong>Prohibit Central Storage of Biometrics Data:</strong> The presence of central storage of sensitive personal information of all residents in one place creates a grave security risk. Even with the most enhanced security measures in place, the quantum of damage in case of a breach is extremely high. Therefore, storage of biometrics must be allowed only on the smart cards that are issued to the residents.<br /><br /></li>
<li><strong>Chain of Trust Model and Audit Trail:</strong> As one of the objects of the legislation is to provide targeted services to beneficiaries and reduce corruption, there should be more accountability measures in place. A chain of trust model must be incorporated in the process of enrolment where individuals and organisations vouch for individuals so that when a ghost is introduced someone has can be held accountable blame is not placed simply on the technology. This is especially important in light of the questions already raised about the deduplication technology. Further, there should be a transparent audit trail made available that allows public access to use of Aadhaar for combating corruption in the supply chain.<br /><br /></li>
<li><strong>Rights of Residents:</strong> There should be specific provisions dealing with cases where an individual is not issued an Aadhaar number or denied access to benefits due to any other factor. Additionally, the Bill should make provisions for residents to access and correct information collected from them, to be notified of data breaches and legal access to information by the Government or its agencies, as matter of right. Further, along with the obligations in Section 8, it should also be mandatory for all requesting entities to notify the individuals of any changes in privacy policy, and providing a mechanism to opt-out.<br /><br /></li>
<li><strong>Establish Appropriate Oversight Mechanisms:</strong> Section 33 currently specifies a procedure for oversight by a committee, however, there are no substantive provisions laid down that shall act as the guiding principles for such oversight mechanisms. The provision should include data minimisation, and “necessity and proportionality” principles as guiding principles for any exceptions to Section 29.<br /><br /></li>
<li><strong>Establish Grievance Redressal and Review Mechanisms:</strong> Currently, there are no grievance redressal mechanism created under the Bill. The power to set up such a mechanism is delegated to the UIDAI under Section 23 (2) (s) of the Bill. However, making the entity administering a project, also responsible for providing for the frameworks to address the grievances arising from the project, severely compromises the independence of the grievance redressal body. An independent national grievance redressal body with state and district level bodies under it, should be set up. Further, the NIAI Bill, 2010, provided for establishing an Identity Review Committee to monitor the usage pattern of Aadhaar numbers. This has been removed in the Aadhaar Bill 2016, and must be restored.</li></ol>
<p> </p>
<h3>Endnotes</h3>
<p><strong>[1]</strong> See: <a href="http://cis-india.org/internet-governance/blog/Flaws_in_the_UIDAI_Process_0.pdf.">http://cis-india.org/internet-governance/blog/Flaws_in_the_UIDAI_Process_0.pdf</a>.</p>
<p><strong>[2]</strong> See: <a href="http://planningcommission.nic.in/reports/genrep/rep_privacy.pdf">http://planningcommission.nic.in/reports/genrep/rep_privacy.pdf</a>.</p>
<p><strong>[3]</strong> See: <a href="http://164.100.47.134/lsscommittee/Finance/15_Finance_42.pdf">http://164.100.47.134/lsscommittee/Finance/15_Finance_42.pdf</a>.</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/list-of-recommendations-on-the-aadhaar-bill-2016'>http://editors.cis-india.org/internet-governance/blog/list-of-recommendations-on-the-aadhaar-bill-2016</a>
</p>
No publisherAmber Sinha, Sumandro Chattapadhyay, Sunil Abraham, and Vanya RakeshUIDBig DataPrivacyInternet GovernanceFeaturedDigital IndiaAadhaarBiometricsHomepage2016-03-21T08:50:09ZBlog EntryPress Release, March 15, 2016: The New Bill Makes Aadhaar Compulsory!
http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-15032016-the-new-bill-makes-aadhaar-compulsory
<b>We published and circulated the following press release on March 15, 2016, to highlight the fact that the Section 7 of the Aadhaar Bill, 2016 states that authentication of the person using her/his Aadhaar number can be made mandatory for the
purpose of disbursement of government subsidies, benefits, and services; and in case the person does not have an Aadhaar number, s/he will have to apply for Aadhaar enrolment. </b>
<p> </p>
<p>Nandan Nilekani, the former chairperson of the Unique Identification Authority of India had repeatedly stated that Aadhaar is not mandatory. However, in the last few years various agencies and departments of the government, both at the central and state level, had made it mandatory in order to be able to avail beneficiary schemes or for the arrangement of salary, provident fund disbursals, promotion, scholarship, opening bank account, marriages and property registrations. In August 2015, the Supreme Court passed an order mandating that the Aadhaar number shall
remain optional for welfare schemes, stating that no person should be denied any benefit for reason of not having an Aadhaar number, barring a few specified services.</p>
<p>The Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act, 2016, however, has not followed this mandate. Section 7 of the Bill states that “a person should be authenticated or give proof of the Aadhaar number to establish his/her identity” “as a condition for receiving subsidy, benefit or service”. Further, it reads, “In the case a person does not have an Aadhaar number, he/she should make an application for enrollment.” The language of the provision is very clear in making enrollment in Aadhaar mandatory, in order to be entitled for welfare services. Section 7 also says that “the person will be offered viable and alternate means of identification for receiving the subsidy, benefit or service. However, these unspecified alternate means will be made available in the event “an Aadhaar number is not assigned”. This language is vague and it is not clear whether it mandates alternate means of identification for those who choose not to apply for an Aadhaar number for any reason. The fact that it does make it mandatory to apply for an Aadhaar number for persons without it, may lead to the presumption that the alternate means are to be made available for those who may have applied for an Aadhaar number but it has not been assigned for any reason. It is also noteworthy that draft legislation is silent on what the “viable and
alternate means of identification” could be. There are a number of means of identification, which are recognised by the state, and a schedule with an inclusive list could have gone a long way in reducing the ambiguity in this provision.</p>
<p>Another aspect of Section 7 which is at odds with the Supreme Court order is that it allows making an Aadhaar number mandatory for “for receipt of a subsidy, benefit or service for which the expenditure is incurred” from the Consolidated Fund of India. The Supreme Court had been very specific in articulating that having an Aadhaar number could not be made compulsory except for “any purpose other than the PDS Scheme and in particular for the purpose of distribution of foodgrains, etc. and cooking fuel, such as kerosene” or for the purpose of the LPG scheme. The restriction in the Supreme Court order was with respect to the welfare schemes, however, instead of specifying the schemes, Section 7 specified the source of expenditure from which subsidies, benefits and services can be funded, making the scope much broader. Section 7, in effect, allows the Central Government to circumvent the Supreme Court
order if they choose to tie more subsidies, benefits and services to the Consolidated Fund of India.</p>
<p>These provisions run counter to the repeated claims of the government for the last six years that Aadhaar is not compulsory, nor is the specification by the Supreme Court for restricting use of Aadhaar to a few services only, reflected anywhere in the Bill. The “viable and alternate means” clause is too vague and inadequate to prevent denial of benefits to those without an Aadhaar number. The sum effect of these factors is to give the Central Government powers to make Aadhaar mandatory, for all practical purposes.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-15032016-the-new-bill-makes-aadhaar-compulsory'>http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-15032016-the-new-bill-makes-aadhaar-compulsory</a>
</p>
No publisherAmber SinhaUIDBig DataPrivacyInternet GovernanceDigital IndiaAadhaarBiometrics2016-03-16T10:11:32ZBlog EntryPress Release, March 11, 2016: The Law cannot Fix what Technology has Broken!
http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-11032016-the-law-cannot-fix-what-technology-has-broken
<b>We published and circulated the following press release on March 11, 2016, as the Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016. This Bill was proposed by finance minister, Mr. Arun Jaitley to give legislative backing to Aadhaar, being implemented by the Unique Identification Authority of India (UIDAI).</b>
<p> </p>
<p>The Lok Sabha passed the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016 today. This Bill was proposed by finance minister, Mr. Arun Jaitley to give legislative backing to Aadhaar, being implemented by the Unique Identification Authority of India (UIDAI).</p>
<p>The Bill was introduced as a money bill and there was no public consultation to evaluate the provisions therein even though there are very serious ramifications for the Right to Privacy and the Right to Association and Assembly. The Bill has made it compulsory for an individual to enrol under Aadhaar in order to receive any subsidy,
benefit or service from the Government. Biometric information that is required for the purpose of enrolment has been deemed "sensitive personal information" and restrictions have been imposed on use, disclosure and sharing of such information for purposes other than authentication, disclosure made pursuant to a court order or in the interest of national security. Here, the Bill has acknowledged the standards of protection of sensitive personal information established under Section 43A of the Information Technology Act, 2000. The Bill has also laid down several penal provisions for acts that include impersonation at the time of enrolment, unauthorised access to the
Central Identities Data Repository, unauthorised use by requesting entity, noncompliance with intimation requirements, etc.</p>
<h3>Key Issues</h3>
<h4>1. Identification without Consent</h4>
<p>Before the Aadhaar project it was not possible for the Indian government to identify citizens without their consent. But once the government has created a national centralized biometric database it will be possible for the government to identify any citizen without their consent. Hi-resolution photography and videography make it trivial for governments and also any other actor to harvest biometrics remotely. In other words, the technology makes consent irrelevant. A German ministers fingerprints were captured by hackers as she spoke using hand gesture at at conference. In a similar manner the government can now identify us both as individuals and also as groups without requiring our cooperation. This has direct implications for the right to privacy as we will be under constant government surveillance in the future as CCTV camera resolutions improve and there will be chilling effects on the
right to free speech and the freedom of association. The only way to fix this is to change the technology configuration and architecture of the project. The law cannot be used as band-aid on really badly designed technology.</p>
<h4>2. Fallible Technology</h4>
<p>The technology used for collection and authentication as been said to be fallible. It is understood that the technology has been feasible for a population of 200 million. The Biometrics Standards Committee of UIDAI has acknowledged the lack of data on how a biometric authentication technology will scale up where the population is about 1.2 billion. Further, a report by 4G Identity Solutions estimates that while in any population, approximately 5% of the people have unreadable fingerprints, in India it could lead to a failure to enroll up to 15% of the population.</p>
<p>We know that the Aadhaar number has been issued to dogs, trees (with the Aadhaar letter containing the photo of a tree). There have been slip-ups in the Aadhaar card enrolment process, some cards have ended up with
pictures of an empty chair, a tree or a dog instead of the actual applicants. An RTI application has revealed that the Unique Identification Authority of India (UIDAI) has identified more than 25,000 duplicate Aadhaar numbers in the country till August 2015.</p>
<p>At the stage of authentication, the accuracy of biometric identification depends on the chance of a false positive— the probability that the identifiers of two persons will match. For the current population of 1.2 billion the expected proportion of duplicates is 1/121, a ratio which is far too high. In a recent paper in EPW by Hans Mathews, a mathematician with CIS, shows that as per UIDAI's own statistics on failure rates, the programme would badly fail to uniquely identify individuals in India. <strong>[1]</strong></p>
<h3>Endnote</h3>
<p><strong>[1]</strong> See: <a href="http://cis-india.org/internet-governance/blog/epw-27-february-2016-hans-varghese-mathews-flaws-in-uidai-process">http://cis-india.org/internet-governance/blog/epw-27-february-2016-hans-varghese-mathews-flaws-in-uidai-process</a></p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-11032016-the-law-cannot-fix-what-technology-has-broken'>http://editors.cis-india.org/internet-governance/blog/press-release-aadhaar-11032016-the-law-cannot-fix-what-technology-has-broken</a>
</p>
No publisherJapreet Grewal and Sunil AbrahamUIDBig DataPrivacyInternet GovernanceDigital IndiaAadhaarBiometrics2016-03-16T10:10:40ZBlog EntryAn Urgent Need for the Right to Privacy
http://editors.cis-india.org/internet-governance/blog/an-urgent-need-for-the-right-to-privacy
<b>Along with a group of individuals and organisations from academia and civil society, we have drafted and are signatories to an open letter addressed to the Union government and urging the same to "urgently take steps to uphold the constitutional basis to the right to privacy and fulfil it’s constitutional and international obligations." Here we publish the text of the open letter. Please follow the link below to support it by joining the signatories.</b>
<p> </p>
<h4><a href="http://goo.gl/forms/hw4huFcc4b" target="_blank">Read and sign the open letter.</a></h4>
<p> </p>
<h2>Text of the Open Letter</h2>
<p>As our everyday lives are conducted increasingly through electronic communications the necessity for privacy protections has also increased. While several countries across the globe have recognised this by furthering the right to privacy of their citizens the Union Government has adopted a regressive attitude towards this core civil liberty. We urge the Union Government to take urgent measures to safeguard the right to privacy in India.</p>
<p>Our concerns are based on a continuing pattern of disregard for the right to privacy by several governments in the past. This trend has increased as can be plainly viewed from the following developments.</p>
<p>In 2015, the Attorney General in the case of *K.S. Puttaswamy v. Union of India*, argued before the Hon’ble Supreme Court that there is no right to privacy under the Constitution of India. The Hon'ble Court was persuaded to re-examine the basis of the right to privacy upsetting 45 years of judicial precedent. This has thrown the constitutional right to privacy in doubt and the several judgements that have been given under it. This includes the 1997 PUCL Telephone Tapping judgement as well. We urge the Union Government to take whatever steps are necessary and urge the Supreme Court to hold that a right to privacy exists under the Constitution of India.</p>
<p>Recently Mr. Arun Jaitley, Minister for Finance introduced the Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Bill, 2016. This bill was passed on March 11, 2016 in the middle of budget discussion on a short notice as a money bill in the Lok Sabha when only 73 of 545 members were present. Its timing and introduction as a money bill prevents necessary scrutiny given the large privacy risks that arise under it. This version of the bill was never put up for public consultation and is being rushed through without adequate discussion. Even substantively it fails to give accountable privacy safeguards while making Aadhaar mandatory for availing any government subsidy, benefit, or service.</p>
<p>We urge the Union Government to urgently take steps to uphold the constitutional basis to the right to privacy and fulfil it’s constitutional and international obligations. We encourage the Government to have extensive public discussions on the Aadhaar Bill before notifying it. We further call upon them to constitute a drafting committee with members of civil society to draft a comprehensive statute as suggested by the Justice A.P. Shah Committee Report of 2012.</p>
<p>Signatories:</p>
<ul><li>Amber Sinha, the Centre for Internet and Society</li>
<li>Japreet Grewal, the Centre for Internet and Society</li>
<li>Joshita Pai, Centre for Communication Governance, National Law University</li>
<li>Raman Jit Singh Chima, Access Now</li>
<li>Sarvjeet Singh, Centre for Communication Governance, National Law University</li>
<li>Sumandro Chattapadhyay, the Centre for Internet and Society</li>
<li>Sunil Abraham, the Centre for Internet and Society</li>
<li>Vanya Rakesh, the Centre for Internet and Society</li></ul>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/an-urgent-need-for-the-right-to-privacy'>http://editors.cis-india.org/internet-governance/blog/an-urgent-need-for-the-right-to-privacy</a>
</p>
No publishersumandroUIDBig DataPrivacyInternet GovernanceDigital IndiaAadhaarBiometrics2016-03-17T07:40:12ZBlog EntryAadhaar Bill 2016 & NIAI Bill 2010 - Comparing the Texts
http://editors.cis-india.org/internet-governance/blog/aadhaar-bill-2016-niai-bill-2010-text-comparison
<b>This is a quick comparison of the texts of the Aadhaar Bill 2016 and the National Identification Authority of India Bill 2010. The new sections in the former are highlighed, and the deleted sections (that were part of the latter) are struck out.</b>
<p> </p>
<iframe src="http://cis-india.github.io/aadhaar-bill-2016/" frameborder="0" height="500px" width="100%"> </iframe>
<p> </p>
<p>Source: <a href="http://cis-india.github.io/aadhaar-bill-2016/">http://cis-india.github.io/aadhaar-bill-2016/</a></p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/aadhaar-bill-2016-niai-bill-2010-text-comparison'>http://editors.cis-india.org/internet-governance/blog/aadhaar-bill-2016-niai-bill-2010-text-comparison</a>
</p>
No publishersumandroUIDAadhaarBig DataPrivacy2016-03-09T11:25:01ZBlog EntrySean McDonald - Ebola: A Big Data Disaster
http://editors.cis-india.org/papers/ebola-a-big-data-disaster
<b>We are proud to initiate the CIS Papers series with a fascinating exploration of humanitarian use of big data and its discontents by Sean McDonald, FrontlineSMS, in the context of utilisation of Call Detail Records for public health response during the Ebola crisis in Liberia. The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modeling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights.</b>
<p> </p>
<h2>Read</h2>
<h4>Download the paper: <a href="https://github.com/cis-india/papers/raw/master/CIS_Papers_2016.01_Sean-McDonald.pdf">PDF</a>.</h4>
<p> </p>
<h2>Preface</h2>
<p>This study titled “Ebola: A Big Data Disaster” by Sean Martin McDonald, undertaken with support from the Open Society Foundation, Ford Foundation, and Media Democracy Fund, explores the use of Big Data in the form of Call Detail Record (CDR) data in humanitarian crisis.</p>
<p> It discusses the challenges of digital humanitarian coordination in health emergencies like the Ebola outbreak in West Africa, and the marked tension in the debate around experimentation with humanitarian technologies and the impact on privacy. McDonald’s research focuses on the two primary legal and human rights frameworks, privacy and property, to question the impact of unregulated use of CDR’s on human rights. It also highlights how the diffusion of data science to the realm of international development constitutes a genuine opportunity to bring powerful new tools to fight crisis and emergencies.</p>
<p>Analysing the risks of using CDRs to perform migration analysis and contact tracing without user consent, as well as the application of big data to disease surveillance is an important entry point into the debate around use of Big Data for development and humanitarian aid. The paper also raises crucial questions of legal significance about the access to information, the limitation of data sharing, and the concept of proportionality in privacy invasion in the public good. These issues hold great relevance in today's time where big data and its emerging role for development, involving its actual and potential uses as well as harms is under consideration across the world.</p>
<p>The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modelling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights.</p>
<p>This study offers an important perspective for us at the Centre for Internet and Society, and our works on Privacy, Big Data, and Big Data for Development, and very productively articulates the risks of adopting solutions to issues important for development without taking into consideration legal implications and the larger impact on human rights. We look forward to continue to critically engage with issues raised by Big Data in the context of human rights and sustainable development, and bring together diverse perspectives on these issues.</p>
<p><em>- Elonnai Hickok, Policy Director, the Centre for Internet and Society</em></p>
<p> </p>
<h2>CIS Papers</h2>
<p>The CIS Papers series publishes open access monographs and discussion pieces that critically contribute to the debates on digital technologies and society. It includes publication of new findings and observations, of work-in-progress, and of critical review of existing materials. These may be authored by researchers at or affiliated to CIS, by external researchers and practitioners, or by a group of discussants. CIS offers editorial support to the selected monographs and discussion pieces. The views expressed, however, are of the authors' alone.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/papers/ebola-a-big-data-disaster'>http://editors.cis-india.org/papers/ebola-a-big-data-disaster</a>
</p>
No publishersumandroBig DataPrivacyOpen DataDisaster ResponseInternet GovernanceHumanitarian ResponseCIS Papers2016-04-21T09:57:26ZBlog EntryDatabase on Big Data and Smart Cities International Standards
http://editors.cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards
<b>The Centre for Internet and Society is in the process of mapping international standards specifically around Big Data, IoT and Smart Cities. Here is a living document containing a database of some of these key globally accepted standards. </b>
<p style="text-align: justify; ">1. <span>International Organisation for Standardization: ISO/IEC JTC 1 Working group on Big Data (WG 9 )</span></p>
<p style="text-align: justify; ">● Background</p>
<p style="text-align: justify; ">- The <a href="http://www.iso.org/">International Organization for Standardization</a> /<a href="http://www.iec.ch/">International Electrotechnical Commission</a> (ISO/IEC) Joint Technical Committee (JTC) <a href="http://www.iso.org/iso/iso_technical_committee?commid=45020">1</a>, Information Technology announced the creation of a Working Group (WG) focused on standardization in connection with big data.</p>
<p style="text-align: justify; ">- JTC 1 is the standards development environment where experts come together to develop worldwide standards on Information and Communication Technology (ICT) for integrating diverse and complex ICT technologies.<a href="#_ftn1" name="_ftnref1"><sup><sup>[1]</sup></sup></a></p>
<p style="text-align: justify; ">- The <a href="https://www.ansi.org/">American National Standards Institute (ANSI)</a> holds the secretariat to JTC 1 and the ANSI-accredited U.S. Technical Advisory Group (TAG) Administrator to JTC 1 is the<a href="http://www.incits.org/">InterNational Committee for Information Technology Standards</a> (INCITS) <a href="#_ftn2" name="_ftnref2"><sup><sup>[2]</sup></sup></a>, an ANSI member and accredited standards developer (ASD). InterNational Committee for Information Technology standards (INCITS) is a technical committee on Big Data to serve as the US Technical Advisory Group (TAG) to JTC 1/WG 9 on Big Data/ pending approval of a New Work Item Proposal (NWIP). The INCITS/Big Data will address standardization in the areas assigned to JTC 1/WG 9. <a href="#_ftn3" name="_ftnref3"><sup><sup>[3]</sup></sup></a></p>
<p style="text-align: justify; ">- Under U.S. leadership, WG 9 on Big Data will serve as the focus of JTC 1's big data standardization program.</p>
<p style="text-align: justify; ">● Objective</p>
<p style="text-align: justify; ">- To identify standardization gaps.</p>
<p style="text-align: justify; ">- Develop foundational standards for Big Data.</p>
<p style="text-align: justify; ">- Develop and maintain liaisons with all relevant JTC 1 entities</p>
<p style="text-align: justify; ">- Grow the awareness of and encourage engagement in JTC 1 Big Data standardization efforts within JTC 1. <a href="#_ftn4" name="_ftnref4"><sup><sup>[4]</sup></sup></a></p>
<p style="text-align: justify; ">● Status</p>
<p style="text-align: justify; ">- JTC 1 appoints Mr. Wo Chang to serve as Convenor of the JTC 1 Working Group on Big Data.</p>
<p style="text-align: justify; ">- The WG has set up a Study Group on Big Data.</p>
<p style="text-align: justify; ">2. <span>International Organisation for Standardization: ISO/IEC JTC 1 Study group on Big Data</span></p>
<p style="text-align: justify; ">● Background</p>
<p style="text-align: justify; ">- The ISO/IEC JTC1 Study Group on Big Data (JTC1 SGBD) was created by Resolution 27 at the November, 2013 JTC1 Plenary at the request of the USA and other national bodies for consideration of Big Data activities across all of JTC 1.</p>
<p style="text-align: justify; ">- A Study Group (SG) is an ISO mechanism by which the convener of a Working Group (WG) under a sub-committee appoints a smaller group of experts to do focused work in a specific area to identify a clear group to focus attention on a major area and expand the manpower of the committee.</p>
<p style="text-align: justify; ">- The goal of an SG is to create a proposal suitable for consideration by the whole WG, and it is the WG that will then decide whether and how to progress the work.<a href="#_ftn5" name="_ftnref5"><sup><sup>[5]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective</p>
<p style="text-align: justify; ">JTC 1 establishes a Study Group on Big Data for consideration of Big Data</p>
<p style="text-align: justify; ">activities across all of JTC 1 with the following objectives:</p>
<p style="text-align: justify; ">- Mapping the existing landscape: Map existing ICT landscape for key technologies and relevant standards /models/studies /use cases and scenarios for Big Data from JTC 1, ISO, IEC and other standards setting organizations,</p>
<p style="text-align: justify; ">- Identify key terms : Identify key terms and definitions commonly used in the area of Big Data,</p>
<p style="text-align: justify; ">- Assess status of big data standardization : Assess the current status of Big Data standardization market requirements, identify standards gaps, and propose standardization priorities to serve as a basis for future JTC 1 work, and</p>
<p style="text-align: justify; ">- Provide a report with recommendations and other potential deliverables to the 2014 JTC 1 Plenary. <a href="#_ftn6" name="_ftnref6"><sup><sup>[6]</sup></sup></a></p>
<p style="text-align: justify; ">● Current Status</p>
<p style="text-align: justify; ">- The study group released a preliminary report in the year 2014, which can be accessed here : <a href="http://www.iso.org/iso/big_data_report-jtc1.pdf">http://www.iso.org/iso/big_data_report-jtc1.pdf</a>.</p>
<p style="text-align: justify; ">3. <span>The National Institute of Standards and Technology Big Data Interoperability Framework : </span></p>
<p style="text-align: justify; ">● Background</p>
<p style="text-align: justify; ">- NIST is leading the development of a Big Data Technology Roadmap which aims to define and prioritize requirements for interoperability, portability, reusability, and extensibility for big data analytic techniques and technology infrastructure to support secure and effective adoption of Big Data.</p>
<p style="text-align: justify; ">- To help develop the ideas in the Big Data Technology Roadmap, NIST is creating the Public Working Group for Big Data which Released Seven Volumes of Big Data Interoperability Framework on September 16, 2015.<a href="#_ftn7" name="_ftnref7"><sup><sup>[7]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective</p>
<p style="text-align: justify; ">- To advance progress in Big Data, the NIST Big Data Public Working Group (NBD-PWG) is working to develop consensus on important, fundamental concepts related to Big Data.</p>
<p style="text-align: justify; ">● Status</p>
<p style="text-align: justify; ">- The results are reported in the NIST Big Data Interoperability Framework series of volumes. Under the framework, seven volumes have been released by NIST, available here:</p>
<p style="text-align: justify; "><a href="http://bigdatawg.nist.gov/V1_output_docs.php">http://bigdatawg.nist.gov/V1_output_docs.php</a></p>
<p style="text-align: justify; ">4. <span>IEEE Standards Association</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- The IEEE Standards Association introduced a number of standards</p>
<p style="text-align: justify; ">related to big-data applications.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">The following standard is under development:</p>
<p style="text-align: justify; ">- <a href="http://standards.ieee.org/develop/project/2413.html">IEEE P2413</a></p>
<p style="text-align: justify; ">"IEEE Standard for an Architectural Framework for the Internet of Things (IoT)" defines the relationships among devices used in industries, including transportation and health care. It also provides a blueprint for data privacy, protection, safety, and security, as well as a means to document and mitigate architecture divergence.<a href="#_ftn8" name="_ftnref8"><sup><sup>[8]</sup></sup></a></p>
<p style="text-align: justify; ">5. <span>ITU</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- The <a href="http://www.itu.int/">International Telecommunications Union (ITU)</a> has announced its first standards for big data services, entitled 'Recommendation ITU-T Y.3600 "Big data - cloud computing based requirements and capabilities"', recognizing the need for strong technical standards considering the growth of big data to ensure that processing tools are able to achieve powerful results in the areas of collection, analysis, visualization, and more.<a href="#_ftn9" name="_ftnref9"><sup><sup>[9]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- Recommendation Y.3600 provides requirements, capabilities and use cases of</p>
<p style="text-align: justify; ">cloud computing based big data as well as its system context. Cloud computing</p>
<p style="text-align: justify; ">based big data provides the capabilities to collect, store, analyze, visualize and</p>
<p style="text-align: justify; ">manage varieties of large volume datasets, which cannot be rapidly transferred</p>
<p style="text-align: justify; ">and analysed using traditional technologies.<a href="#_ftn10" name="_ftnref10"><sup><sup>[10]</sup></sup></a></p>
<p style="text-align: justify; ">- It also outlines how cloud computing systems can be leveraged to provide big-data services.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- The standard was relseased in the year 2015 and is avaiabe here: <a href="http://www.itu.int/rec/T-REC-Y.3600-201511-I">http://www.itu.int/rec/T-REC-Y.3600-201511-I</a> .</p>
<p style="text-align: justify; "><b>Smart cities</b></p>
<p style="text-align: justify; ">1. <span>ISO Standards on Smart Cities</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- ISO, the International Organization for Standardization, established a strategic advisory group in 2014 for smart cities, comprised of a wide range of international experts to advise ISO on how to coordinate current and future Smart City standardization activities, in cooperation with other international standards organizations, to benefit the market.<a href="#_ftn11" name="_ftnref11"><sup><sup>[11]</sup></sup></a></p>
<p style="text-align: justify; ">- Seven countries, China, Germany, UK, France, Japan, Korea and USA, are currently involved in the research.</p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- The main aims of which are to formulate a definition of a Smart City</p>
<p style="text-align: justify; ">- Identify current and future ISO standards projects relating to Smart Cities</p>
<p style="text-align: justify; ">- Examine involvement of potential stakeholders, city requirements, potential interface problems. <a href="#_ftn12" name="_ftnref12"><sup><sup>[12]</sup></sup></a></p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- ISO/TC 268, which is focused on sustainable development in communities, has one working group developing city indicators and other developing metrics for smart community infrastructures. In early 2016 this committee will be joined by another - IEC - systems committee. The first standard produced by ISO/TC 268 is ISO/TR 37150:2014.</p>
<p style="text-align: justify; ">- ISO/TR 37150:2014 Smart community infrastructures -- Review of existing activities relevant to metrics: this standard provides a review of existing activities relevant to metrics for smart community infrastructures. The concept of smartness is addressed in terms of performance relevant to technologically implementable solutions, in accordance with sustainable development and resilience of communities, as defined in ISO/TC 268. ISO/TR 37150:2014 addresses community infrastructures such as energy, water, transportation, waste and information and communications technology (ICT). It focuses on the technical aspects of existing activities which have been published, implemented or discussed. Economic, political or societal aspects are not analyzed in ISO/TR 37150:2014.<a href="#_ftn13" name="_ftnref13"><sup><sup>[13]</sup></sup></a></p>
<p style="text-align: justify; ">- <a href="https://www.iso.org/obp/ui/#iso:std:iso:37120:ed-1:v1:en">ISO 37120:2014</a> provides city leaders and citizens a set of clearly defined city performance indicators and a standard approach for measuring each. Though some indicators will be more helpful for cities than others, cities can now consistently apply these indicators and accurately benchmark their city services and quality of life against other cities.<a href="#_ftn14" name="_ftnref14"><sup><sup>[14]</sup></sup></a> This new international standard was developed using the framework of the <a href="http://www.cityindicators.org/">Global City Indicators Facility (GCIF)</a> that has been extensively tested by more than 255 cities worldwide. This is a demand-led standard, driven and created by cities, for cities. ISO 37120 defines and establishes definitions and methodologies for a set of indicators to steer and measure the performance of city services and quality of life. The standard includes a comprehensive set of 100 indicators - of which 46 are core - that measures a city's social, economic, and environmental performance. <a href="#_ftn15" name="_ftnref15"><sup><sup>[15]</sup></sup></a></p>
<p style="text-align: justify; ">The GCIF global network, supports the newly constituted World Council on City Data - a sister organization of the GCI/GCIF - which allows for independent, third party verification of ISO 37120 data.<a href="#_ftn16" name="_ftnref16"><sup><sup>[16]</sup></sup></a></p>
<p style="text-align: justify; ">- <a href="http://www.iso.org/obp/ui/#iso:std:iso:ts:37151:ed-1:v1:en">ISO/TS 37151</a> and ISO/TR 37152 Smart community infrastructures -- Common framework for development & operation: outlines 14 categories of basic community needs (from the perspective of residents, city managers and the environment) to measure the performance of smart community infrastructures. These are typical community infrastructures like energy, water, transportation, waste and information and communication technology systems, which have been optimized with sustainable development and resilience in mind. <a href="#_ftn17" name="_ftnref17"><sup><sup>[17]</sup></sup></a> The committee responsible for this document is ISO/TC 268, Sustainable development in communities, Subcommittee SC 1, Smart community infrastructures. The objective is to develop international consensus on a harmonised metrics to evaluate the smartness of key urban infrastructure.<a href="#_ftn18" name="_ftnref18"><sup><sup>[18]</sup></sup></a></p>
<p style="text-align: justify; ">- ISO 37101 Sustainable development of communities -- Management systems -- Requirements with guidance for resilience and smartness : By setting out requirements and guidance to attain sustainability with the support of methods and tools including smartness and resilience, it can help communities improve in a number of areas such as: Developing holistic and integrated approaches instead of working in silos (which can hinder sustainability), Fostering social and environmental changes, Improving health and wellbeing, Encouraging responsible resource use and Achieving better governance. <a href="#_ftn19" name="_ftnref19"><sup><sup>[19]</sup></sup></a> The objective is to develop a Management System Requirements Standard reflecting consensus on an integrated, cross-sector approach drawing on existing standards and best practices.</p>
<p style="text-align: justify; ">- ISO 37102 Sustainable development & resilience of communities - Vocabulary . The objective is to establish a common set of terms and definitions for standardization in sustainable development, resilience and smartness in communities, cities and territories since there is pressing need for harmonization and clarification. This would provide a common language for all interested parties and stakeholders at the national, regional and international levels and would lead to improved ability to conduct benchmarks and to share experiences and best practices.</p>
<p style="text-align: justify; ">- ISO/TR 37121 Inventory & review of existing indicators on sustainable development & resilience in cities : A common set of indicators useable by every city in the world and covering most issues related to sustainability, resilience and quality of life in cities. <a href="#_ftn20" name="_ftnref20"><sup><sup>[20]</sup></sup></a></p>
<p style="text-align: justify; ">- ISO/TR 12859:2009 gives general guidelines to developers of intelligent transport systems (ITS) standards and systems on data privacy aspects and associated legislative requirements for the development and revision of ITS standards and systems. <a href="#_ftn21" name="_ftnref21"><sup><sup>[21]</sup></sup></a></p>
<p style="text-align: justify; ">2. <span>International Organisation for Standardization: ISO/IEC JTC 1 Working group on Smart Cities (WG 11 )</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- Serve as the focus of and proponent for JTC 1's Smart Cities standardization program and works for development of foundational standards for the use of ICT in Smart Cities - including the Smart City ICT Reference Framework and an Upper Level Ontology for Smart Cities - for guiding Smart Cities efforts throughout JTC 1 upon which other standards can be developed.<a href="#_ftn22" name="_ftnref22"><sup><sup>[22]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- To develop a set of ICT related indicators for Smart Cities in collaboration with ISO/TC 268.</p>
<p style="text-align: justify; ">- Identify JTC 1 (and other organization) subgroups developing standards and related material that contribute to Smart Cities.</p>
<p style="text-align: justify; ">- Grow the awareness of, and encourage engagement in, JTC 1 Smart Cities standardization efforts within JTC 1.</p>
<p style="text-align: justify; ">● Status</p>
<p style="text-align: justify; ">- Ms Yuan Yuan is the Convenor of this Working group.</p>
<p style="text-align: justify; ">- The purpose was to provide a report with recommendations to the JTC 1 Plenary in the year 2014, to which a preliminary report was submitted. <a href="#_ftn23" name="_ftnref23"><sup><sup>[23]</sup></sup></a></p>
<p style="text-align: justify; ">3. <span>International Organisation for Standardization: ISO/IEC JTC 1 Study Group (SG1) on Smart Cities </span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- The Study Group (SG) - Smart Cities was established in 2013<a href="#_ftn24" name="_ftnref24"><sup><sup>[24]</sup></sup></a> SG 1 will explicitly consider the work going on in the following committees: ISO/TMB/AG on Smart Cities, IEC/SEG 1, ITU-T/FG SSC and ISO/TC 268. <a href="#_ftn25" name="_ftnref25"><sup><sup>[25]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective :</p>
<p style="text-align: justify; ">- To examine the needs and potentials for standardization in this area.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- SG 1 is paying particular attention to monitoring cloud computing activities, which it sees as the key element of the Smart Cities infrastructure. DIN's Information Technology and Selected IT Applications Standards Committee (NIA (www.nia.din.de)) is formally responsible for ISO/IEC JTC1 /SG 1, but an autonomous national mirror committee on Smart Cities does not yet exist and the work is being overseen by DIN's Smart Grid steering body. <a href="#_ftn26" name="_ftnref26"><sup><sup>[26]</sup></sup></a></p>
<p style="text-align: justify; ">- A preliminary report has been released in the 2014, available here- <a href="http://www.iso.org/iso/smart_cities_report-jtc1.pdf">http://www.iso.org/iso/smart_cities_report-jtc1.pdf</a></p>
<p style="text-align: justify; ">4. <span>ITU</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- ITU members have established an ITU-T Study Group titled "ITU-T Study Group 20: IoT and its applications, including smart cities and communities" <a href="#_ftn27" name="_ftnref27"><sup><sup>[27]</sup></sup></a></p>
<p style="text-align: justify; ">- ITU-T has also established a Focus Group on Smart Sustainable Cities (FG-SSC).</p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- The study group will address the standardization requirements of Internet of Things (IoT) technologies, with an initial focus on IoT applications in smart cities.</p>
<p style="text-align: justify; ">- The focus group shall assess the standardization requirements of cities aiming to boost their social, economic and environmental sustainability through the integration of information and communication technologies (ICTs) in their infrastructures and operations.</p>
<p style="text-align: justify; ">- The Focus Group will act as an open platform for smart-city stakeholders - such as municipalities; academic and research institutes; non-governmental organizations (NGOs); and ICT organizations, industry forums and consortia - to exchange knowledge in the interests of identifying the standardized frameworks needed to support the integration of ICT services in smart cities.<a href="#_ftn28" name="_ftnref28"><sup><sup>[28]</sup></sup></a></p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- The study group will develop standards that leverage IoT technologies to address urban-development challenges.</p>
<p style="text-align: justify; ">- The FG-SSC concluded its work in May 2015 by approving 21 Technical Specifications and Reports. <a href="#_ftn29" name="_ftnref29"><sup><sup>[29]</sup></sup></a></p>
<p style="text-align: justify; ">- So far, ITU-T SG 5 FG-SSC has issued the following reports- Technical report "An overview of smart sustainable cities and the role of information and communication technologies", Technical report "Smart sustainable cities: an analysis of definitions", Technical report "Electromagnetic field (EMF) considerations in smart sustainable cities", Technical specifications "Overview of key performance indicators in smart sustainable cities", Technical report "Smart water management in cities".<a href="#_ftn30" name="_ftnref30"><sup><sup>[30]</sup></sup></a></p>
<p style="text-align: justify; ">5. <a href="http://pripareproject.eu/">PRIPARE Project </a>:</p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; "><a name="h.h6pbyhgvwgvj"></a> - The 7001 - PRIPARE Smart City Strategy is to to ensure that ICT solutions integrated in EIP smart cities will be compliant with future privacy regulation.</p>
<p style="text-align: justify; "><a name="h.lhbkbgn0b1jv"></a> - PRIPARE aims to develop a privacy and security-by-design software and systems engineering methodology, using the combined expertise of the research community and taking into account multiple viewpoints (advocacy, legal, engineering, business).</p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- The mission of PRIPARE is to facilitate the application of a privacy and security-by-design methodology that will contribute to the advent of unhindered usage of Internet against disruptions, censorship and surveillance, support its practice by the ICT research community to prepare for industry practice and foster risk management culture through educational material targeted to a diversity of stakeholders.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- Liaison is currently on-going so that it becomes a standard (OASIS and ISO).<a href="#_ftn31" name="_ftnref31"><sup><sup>[31]</sup></sup></a></p>
<p style="text-align: justify; ">6. <span>BSI-UK</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- In the UK, the British Standards Institution (BSI) has been commissioned by the UK Department of Business, Innovation and Skills (BIS) to conceive a Smart Cities Standards Strategy to identify vectors of smart city development where standards are needed.</p>
<p style="text-align: justify; ">- The standards would be developed through a consensus-driven process under the BSI to ensure good practise is shared between all the actors. <a href="#_ftn32" name="_ftnref32"><sup><sup>[32]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">The BIS launched the City's Standards Institute to bring together cities and key</p>
<p style="text-align: justify; ">industry leaders and innovators :</p>
<p style="text-align: justify; ">- To work together in identifying the challenges facing cities,</p>
<p style="text-align: justify; ">- Providing solutions to common problems, and</p>
<p style="text-align: justify; ">- Defining the future of smart city standards.<a href="#_ftn33" name="_ftnref33"><sup><sup>[33]</sup></sup></a></p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">The following standards and publications help address various issues for a city to</p>
<p style="text-align: justify; ">become a smart city:</p>
<p style="text-align: justify; ">- The development of a standard on <a href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-180-smart-cities-terminology/"> Smart city terminology (PAS 180) </a></p>
<p style="text-align: justify; ">- The development of a <a href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-181-smart-cities-framework/"> Smart city framework standard (PAS 181) </a></p>
<p style="text-align: justify; ">- The development of a <a href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PAS-182-smart-cities-data-concept-model/"> Data concept model for smart cities (PAS 182) </a></p>
<p style="text-align: justify; ">- A <a href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PD-8100-smart-cities-overview/"> Smart city overview document (PD 8100) </a></p>
<p style="text-align: justify; ">- A <a href="http://www.bsigroup.com/en-GB/smart-cities/Smart-Cities-Standards-and-Publication/PD-8101-smart-cities-planning-guidelines/"> Smart city planning guidelines document (PD 8101) </a></p>
<p style="text-align: justify; ">- BS 8904 Guidance for community sustainable development provides a decision-making framework that will help setting objectives in response to the needs and aspirations of city stakeholders</p>
<p style="text-align: justify; ">- BS 11000 Collaborative relationship management</p>
<p style="text-align: justify; ">- BSI BIP 2228:2013 Inclusive urban design - A guide to creating accessible public spaces.</p>
<p style="text-align: justify; ">7. <span>Spain</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- <a href="http://www.en.aenor.es/">AENOR</a>, the Spanish standards developing organization (SDO), has issued <a href="http://www.en.aenor.es/aenor/normas/ctn/fichactn.asp?codigonorm=AEN/CTN%20178">two new standards</a> on smart cities: the UNE 178303 and UNE-ISO 37120. These standards joined the already published UNE 178301.</p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- The texts, prepared by the Technical Committee of Standardization of AENOR on Smart Cities (AEN / CTN 178) and sponsored by the SETSI (Secretary of State for Telecommunications and Information Society of the Ministry of Industry, Energy and Tourism), aim to encourage the development of a new model of urban services management based on efficiency and sustainability.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">Some of the standards that have been developed are:</p>
<p style="text-align: justify; ">- UNE 178301 on Open Data evaluates the maturity of open data created or held by the public sector so that its reuse is provided in the field of Smart Cities.</p>
<p style="text-align: justify; ">- UNE 178303 establishes the requirements for proper management of municipal assets.</p>
<p style="text-align: justify; ">- UNE-ISO 37120 which collects the international urban sustainability indicators.</p>
<p style="text-align: justify; ">- Following the publication of these standards, 12 other draft standards on Smart Cities have just been made public, most of them corresponding to public services such as water, electricity and telecommunications, and multiservice city networks. <a href="#_ftn34" name="_ftnref34"><sup><sup>[34]</sup></sup></a></p>
<p style="text-align: justify; ">8. <span>China</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">Several national standardization committees and consortia have started</p>
<p style="text-align: justify; ">standardization work on Smart Cities, including:</p>
<p style="text-align: justify; ">- China National IT Standardization TC (NITS),</p>
<p style="text-align: justify; ">- China National CT Standardization TC,</p>
<p style="text-align: justify; ">- China National Intelligent Transportation System Standardization TC,</p>
<p style="text-align: justify; ">- China National TC on Digital Technique of Intelligent Building and Residence Community of Standardization Administration, China Strategic Alliance of Smart City Industrial Technology Innovation<a href="#_ftn35" name="_ftnref35"><sup><sup>[35]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- In the year 2014, all the ministries involved in building smart cities in China joined with the Standardization Administration of China to create working groups whose job is to manage and standardize smart city development, though their activities have not been publicized. <a href="#_ftn36" name="_ftnref36"><sup><sup>[36]</sup></sup></a></p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- China will continue to promote international standards in building smart cities and improve the competitiveness of its related industries in global market.</p>
<p style="text-align: justify; ">- Also, China's Standardization Administration has joined hands with National Development and Reform Commission, Ministry of Housing and Urban-Rural Development and Ministry of Industry and Information Technology in establishing and implementing standards for smart cities.</p>
<p style="text-align: justify; ">- When building smart cities, the country will adhere to the ISO 37120 and by the year 2020, China will establish 50 national standards on smart cities. <a href="#_ftn37" name="_ftnref37"><sup><sup>[37]</sup></sup></a></p>
<p style="text-align: justify; ">9. <span>Germany</span></p>
<p style="text-align: justify; ">● Background :</p>
<p style="text-align: justify; ">- Member of European Innovation Partnership (EIP) for Smart Cities and Communities DKE (German Commission for Electrical, Electronic & Information Technologies) and DIN (GermanInstitute for Standardization) have developed a joint roadmap and Smart Cities recommendations for action in Germany.</p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- Its purpose is to highlight the need for standards and to serve as a strategic template for national and international standardization work in the field of smart city technology.</p>
<p style="text-align: justify; ">- The Standardization Roadmap highlights the main activities required to create smart cities. <a href="#_ftn38" name="_ftnref38"><sup><sup>[38]</sup></sup></a></p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- An updated version of the standardization roadmap was released in the year 2015. <a href="#_ftn39" name="_ftnref39"><sup><sup>[39]</sup></sup></a></p>
<p style="text-align: justify; ">10. <span>Poland</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- A coordination group on Smart and Sustainable Cities and Communities (SSCC) was set up in the beginning of 2014 to monitor any national standardization activities.</p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- It was decided to put forward a proposal to form a group at the Polish Committee for Standardization (PKN) providing recommendations for smart sustainable city standardization in Poland.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">It has two thematic groups:</p>
<p style="text-align: justify; ">- GT 1-2 on terminology and Technical Bodies in PKN Its scope covers a collection of English terms and their Polish equivalents related to smart and sustainable development of cities and communities to allow better communication among various smart city stakeholders. This includes the preparation of the list of Technical Bodies (OT) in PKN involved in standardization activities related to specific aspects of smart and sustainable local development and making proposals concerning the allocation of standardization works to the relevant OT in PKN.</p>
<p style="text-align: justify; ">- GT 3 for gathering information and the development and implementation of a work programme Its scope includes identifying stakeholders in Poland, and gathering information on any national "smart city" initiatives having an impact on environment-friendly development, sustainability, and liveability of a city. The group is also tasked with developing a work programme for GZ 1 based on identified priorities for Poland. Finally, its aim is to conduct communication and dissemination of activities to make the results of GZ 1 visible. <a href="#_ftn40" name="_ftnref40"><sup><sup>[40]</sup></sup></a></p>
<p style="text-align: justify; ">11. <span>Europe</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- In 2012, the European standardization organizations CEN and CENELEC founded the Smart and Sustainable Cities and Communities Coordination Group (SSCC-CG), which is a Coordination Group established to coordinate standardization activities and foster collaboration around standardization work. <a href="#_ftn41" name="_ftnref41"><sup><sup>[41]</sup></sup></a></p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- The aim of the CEN-CENELEC-ETSI (SSCC-CG) is to coordinate and promote European standardization activities relating to Smart Cities and to advise the CEN and CENELEC (Technical) and ETSI Boards on standardization activities in the field of Smart and Sustainable Cities and Communities.</p>
<p style="text-align: justify; ">- The scope of the SSCC-CG is to advise on European interests and needs relating to standardization on Smart and Sustainable cities and communities.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">- Originally conceived to be completed by the end of 2014, SSCC-CG's mandate has been extended by the European standards organizations CEN, CENELEC and ETSI by a further two years and will run until the end of 2016.<a href="#_ftn42" name="_ftnref42"><sup><sup>[42]</sup></sup></a></p>
<p style="text-align: justify; ">- The SSCC-CG does not develop standards, but reports directly to the management boards of the standardization organizations and plays an advisory role. Current members of the SSCC.CG include representatives of the relevant technical committees, the CEN/CENELEC secretariat, the European Commission, the European associations and the national standardization organizations.<a href="#_ftn43" name="_ftnref43"><sup><sup>[43]</sup></sup></a></p>
<p style="text-align: justify; ">- CEN/CENELEC/ETSI Joint Working Group on Standards for Smart Grids: The aim of this document is to provide a strategic report which outlines the standardization requirements for implementing the European vision of smart grids, especially taking into account the initiatives by the Smart Grids Task Force of the European Commission. It provides an overview of standards, current activities, fields of action, international cooperation and strategic recommendations<a href="#_ftn44" name="_ftnref44"><sup><sup>[44]</sup></sup></a></p>
<p style="text-align: justify; ">12. <span>Singapore</span></p>
<p style="text-align: justify; ">● Background:</p>
<p style="text-align: justify; ">- In the year 2015, SPRING Singapore, the Infocomm Development Authority of Singapore (IDA) and the Information Technology Standards Committee (ITSC), under the purview of the Singapore Standards Council (SSC), have laid out an Internet of Things (IoT) Standards Outline in support of Singapore's Smart Nation initiative.</p>
<p style="text-align: justify; ">● Objective:</p>
<p style="text-align: justify; ">- Realising importance of standards in laying the foundation for the nation empowered by big data, analytics technology and sensor networks in light of Singapore's vision of becoming a Smart Nation.</p>
<p style="text-align: justify; ">● Status:</p>
<p style="text-align: justify; ">Three types of standards - sensor network standards, IoT foundational standards and domain-specific standards - have been identified under the IoT Standards Outline. Singapore actively participates in the ISO Technical Committee (TC) working on smart city standards.<a href="#_ftn45" name="_ftnref45"><sup><sup>[45]</sup></sup></a></p>
<div style="text-align: justify; ">
<hr />
<div id="ftn1">
<p><a href="#_ftnref1" name="_ftn1"><sup><sup>[1]</sup></sup></a> ISO/IEC JTC 1, Information Technology, http://www.iso.org/iso/jtc1_home.html</p>
</div>
<div id="ftn2">
<p><a href="#_ftnref2" name="_ftn2"><sup><sup>[2]</sup></sup></a> The InterNational Committee for Information Technology Standards, JTC 1 Working Group on Big Data, http://www.incits.org/committees/big-data</p>
</div>
<div id="ftn3">
<p><a name="h.h17u2luhqusv"></a> <a href="#_ftnref3" name="_ftn3"><sup><sup>[3]</sup></sup></a> ISO/IEC JTC 1 Forms Two Working Groups on Big Data and Internet of Things, 27th January 2015, https://www.ansi.org/news_publications/news_story.aspx?menuid=7&articleid=5b101d27-47b5-4540-bca3-657314402591</p>
</div>
<div id="ftn4">
<p><a href="#_ftnref4" name="_ftn4"><sup><sup>[4]</sup></sup></a> JTC 1 November 2014 Resolution 28 - Establishment of a Working Group on Big Data, and Call for Participation, 20th January 2015, http://jtc1sc32.org/doc/N2601-2650/32N2625-J1N12445_JTC1_Big_Data-call_for_participation.pdf</p>
</div>
<div id="ftn5">
<p><a href="#_ftnref5" name="_ftn5"><sup><sup>[5]</sup></sup></a> SD-3: Study Group Organizational Information, https://isocpp.org/std/standing-documents/sd-3-study-group-organizational-information</p>
</div>
<div id="ftn6">
<p><a href="#_ftnref6" name="_ftn6"><sup><sup>[6]</sup></sup></a> ISO/IEC JTC 1 Study Group on Big Data (BD-SG), http://jtc1bigdatasg.nist.gov/home.php</p>
</div>
<div id="ftn7">
<p><a href="#_ftnref7" name="_ftn7"><sup><sup>[7]</sup></sup></a> NIST Released V1.0 Seven Volumes of Big Data Interoperability Framework (September 16, 2015),http://bigdatawg.nist.gov/home.php</p>
</div>
<div id="ftn8">
<p><a href="#_ftnref8" name="_ftn8"><sup><sup>[8]</sup></sup></a> Standards That Support Big Data, Monica Rozenfeld, 8th September 2014, http://theinstitute.ieee.org/benefits/standards/standards-that-support-big-data</p>
</div>
<div id="ftn9">
<p><a href="#_ftnref9" name="_ftn9"><sup><sup>[9]</sup></sup></a> ITU releases first ever big data standards, Madolyn Smith, 21st December 2015, http://datadrivenjournalism.net/news_and_analysis/itu_releases_first_ever_big_data_standards#sthash.m3FBt63D.dpuf</p>
</div>
<div id="ftn10">
<p><a href="#_ftnref10" name="_ftn10"><sup><sup>[10]</sup></sup></a> ITU-T Y.3600 (11/2015) Big data - Cloud computing based requirements and capabilities, http://www.itu.int/itu-t/recommendations/rec.aspx?rec=12584</p>
</div>
<div id="ftn11">
<p><a href="#_ftnref11" name="_ftn11"><sup><sup>[11]</sup></sup></a> ISO Strategic Advisory Group on Smart Cities - Demand-side survey, March 2015, http://www.platform31.nl/uploads/media_item/media_item/41/62/Toelichting_ISO_Smart_cities_Survey-1429540845.pdf</p>
</div>
<div id="ftn12">
<p><a href="#_ftnref12" name="_ftn12"><sup><sup>[12]</sup></sup></a> The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf</p>
</div>
<div id="ftn13">
<p><a href="#_ftnref13" name="_ftn13"><sup><sup>[13]</sup></sup></a> ISO/TR 37150:2014 Smart community infrastructures -- Review of existing activities relevant to metrics, http://www.iso.org/iso/catalogue_detail?csnumber=62564</p>
</div>
<div id="ftn14">
<p><a name="h.vnj2x6i94wax"></a> <a href="#_ftnref14" name="_ftn14"><sup><sup>[14]</sup></sup></a> Dissecting ISO 37120: Why this new smart city standard is good news for cities, 30th July 2014, http://smartcitiescouncil.com/article/dissecting-iso-37120-why-new-smart-city-standard-good-news-cities</p>
</div>
<div id="ftn15">
<p><a href="#_ftnref15" name="_ftn15"><sup><sup>[15]</sup></sup></a> World Council for City Data, http://www.dataforcities.org/wccd/</p>
</div>
<div id="ftn16">
<p><a href="#_ftnref16" name="_ftn16"><sup><sup>[16]</sup></sup></a> Global City Indicators Facility, http://www.cityindicators.org/</p>
</div>
<div id="ftn17">
<p><a href="#_ftnref17" name="_ftn17"><sup><sup>[17]</sup></sup></a> How to measure the performance of smart cities, Maria Lazarte, 5th October 2015</p>
<p>http://www.iso.org/iso/home/news_index/news_archive/news.htm?refid=Ref2001</p>
</div>
<div id="ftn18">
<p><a href="#_ftnref18" name="_ftn18"><sup><sup>[18]</sup></sup></a> http://iet.jrc.ec.europa.eu/energyefficiency/sites/energyefficiency/files/files/documents/events/slideslairoctober2014.pdf</p>
</div>
<div id="ftn19">
<p><a href="#_ftnref19" name="_ftn19"><sup><sup>[19]</sup></sup></a> A standard for improving communities reaches final stage, Clare Naden, 12th February 2015,</p>
<p>http://www.iso.org/iso/news.htm?refid=Ref1932</p>
</div>
<div id="ftn20">
<p><a href="#_ftnref20" name="_ftn20"><sup><sup>[20]</sup></sup></a> http://iet.jrc.ec.europa.eu/energyefficiency/sites/energyefficiency/files/files/documents/events/slideslairoctober2014.pdf</p>
</div>
<div id="ftn21">
<p><a href="#_ftnref21" name="_ftn21"><sup><sup>[21]</sup></sup></a> ISO/TR 12859:2009 Intelligent transport systems -- System architecture -- Privacy aspects in ITS standards and systems, http://www.iso.org/iso/catalogue_detail.htm?csnumber=52052</p>
</div>
<div id="ftn22">
<p><a href="#_ftnref22" name="_ftn22"><sup><sup>[22]</sup></sup></a> ISO/IEC JTC 1 Information technology, WG 11 Smart Cities, http://www.iec.ch/dyn/www/f?p=103:14:0::::FSP_ORG_ID,FSP_LANG_ID:12973,25</p>
</div>
<div id="ftn23">
<p><a href="#_ftnref23" name="_ftn23"><sup><sup>[23]</sup></sup></a> Work of ISO/IEC JTC1 Smart Ci4es Study group , https://interact.innovateuk.org/documents/3158891/17680585/2+JTC1+Smart+Cities+Group/e639c7f6-4354-4184-99bf-31abc87b5760</p>
</div>
<div id="ftn24">
<p><a href="#_ftnref24" name="_ftn24"><sup><sup>[24]</sup></sup></a> JTC1 SAC - Meeting 13 , February 2015, http://www.finance.gov.au/blog/2015/08/05/jtc1-sac-meeting-13-february-2015/</p>
</div>
<div id="ftn25">
<p><a href="#_ftnref25" name="_ftn25"><sup><sup>[25]</sup></sup></a> The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf</p>
</div>
<div id="ftn26">
<p><a href="#_ftnref26" name="_ftn26"><sup><sup>[26]</sup></sup></a> The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf</p>
</div>
<div id="ftn27">
<p><a href="#_ftnref27" name="_ftn27"><sup><sup>[27]</sup></sup></a> ITU standards to integrate Internet of Things in Smart Cities, 10th June 2015, https://www.itu.int/net/pressoffice/press_releases/2015/22.aspx</p>
</div>
<div id="ftn28">
<p><a href="#_ftnref28" name="_ftn28"><sup><sup>[28]</sup></sup></a> ITU-T Focus Group Smart Sustainable Cities, https://www.itu.int/dms_pub/itu-t/oth/0b/04/T0B0400004F2C01PDFE.pdf</p>
</div>
<div id="ftn29">
<p><a href="#_ftnref29" name="_ftn29"><sup><sup>[29]</sup></sup></a> Focus Group on Smart Sustainable Cities, http://www.itu.int/en/ITU-T/focusgroups/ssc/Pages/default.aspx</p>
</div>
<div id="ftn30">
<p><a href="#_ftnref30" name="_ftn30"><sup><sup>[30]</sup></sup></a> The German Standardization Roadmap Smart City Version 1.1, May 2015, https://www.vde.com/en/dke/std/documents/nr_smartcity_en_v1.1.pdf</p>
</div>
<div id="ftn31">
<p><a href="#_ftnref31" name="_ftn31"><sup><sup>[31]</sup></sup></a> 7001 - PRIPARE Smart City Strategy, https://eu-smartcities.eu/commitment/7001</p>
</div>
<div id="ftn32">
<p><a href="#_ftnref32" name="_ftn32"><sup><sup>[32]</sup></sup></a> Financing Tomorrow's Cities: How Standards Can Support the Development of Smart Cities, http://www.longfinance.net/groups7/viewdiscussion/72-financing-financing-tomorrow-s-cities-how-standards-can-support-the-development-of-smart-cities.html?groupid=3</p>
</div>
<div id="ftn33">
<p><a href="#_ftnref33" name="_ftn33"><sup><sup>[33]</sup></sup></a> BSI-Smart Cities, http://www.bsigroup.com/en-GB/smart-cities/</p>
</div>
<div id="ftn34">
<p><a href="#_ftnref34" name="_ftn34"><sup><sup>[34]</sup></sup></a> New Set of Smart Cities Standards in Spain, https://eu-smartcities.eu/content/new-set-smart-cities-standards-spain</p>
</div>
<div id="ftn35">
<p><a href="#_ftnref35" name="_ftn35"><sup><sup>[35]</sup></sup></a> Technical Report, M2M & ICT Enablement in Smart Cities, Telecommunication Engineering Centre, Department of Telecommunications, Ministry of Communications and Information Technology, Government of India, November 2015, http://tec.gov.in/pdf/M2M/ICT%20deployment%20and%20strategies%20for%20%20Smart%20Cities.pdf</p>
</div>
<div id="ftn36">
<p><a href="#_ftnref36" name="_ftn36"><sup><sup>[36]</sup></sup></a> Smart City Development in China, Don Johnson, 17th June 2014, http://www.chinabusinessreview.com/smart-city-development-in-china/</p>
</div>
<div id="ftn37">
<p><a href="#_ftnref37" name="_ftn37"><sup><sup>[37]</sup></sup></a> China to continue develop standards on smart cities, 17th December 2015, http://www.chinadaily.com.cn/world/2015wic/2015-12/17/content_22732897.htm</p>
</div>
<div id="ftn38">
<p><a href="#_ftnref38" name="_ftn38"><sup><sup>[38]</sup></sup></a> The German Standardization Roadmap Smart City, April 2014, https://www.dke.de/de/std/documents/nr_smart%20city_en_version%201.0.pdf</p>
</div>
<div id="ftn39">
<p><a href="#_ftnref39" name="_ftn39"><sup><sup>[39]</sup></sup></a> This version of the Smart City Standardization Roadmap, Version 1.1, is an incremental revision of Version 1.0. In Version 1.1, a special focus is placed on giving an overview of current standardization activities and interim results, thus illustrating German ambitions in this area.</p>
</div>
<div id="ftn40">
<p><a href="#_ftnref40" name="_ftn40"><sup><sup>[40]</sup></sup></a> SSCC-CG Final report Smart and Sustainable Cities and Communities Coordination Group, January 2015, https://www.etsi.org/images/files/SSCC-CG_Final_Report-recommendations_Jan_2015.pdf</p>
</div>
<div id="ftn41">
<p><a href="#_ftnref41" name="_ftn41"><sup><sup>[41]</sup></sup></a> Orchestrating infrastructure for sustainable Smart Cities , http://www.iec.ch/whitepaper/pdf/iecWP-smartcities-LR-en.pdf</p>
</div>
<div id="ftn42">
<p><a href="#_ftnref42" name="_ftn42"><sup><sup>[42]</sup></sup></a> Urbanization- Why do we need standardization?, http://www.din.de/en/innovation-and-research/smart-cities-en</p>
</div>
<div id="ftn43">
<p><a href="#_ftnref43" name="_ftn43"><sup><sup>[43]</sup></sup></a> CEN-CENELEC-ETSI Coordination Group 'Smart and Sustainable Cities and Communities' (SSCC-CG), http://www.cencenelec.eu/standards/Sectors/SmartLiving/smartcities/Pages/SSCC-CG.aspx</p>
</div>
<div id="ftn44">
<p><a href="#_ftnref44" name="_ftn44"><sup><sup>[44]</sup></sup></a> Final report of the CEN/CENELEC/ETSI Joint Working Group on Standards for Smart Grids, https://www.etsi.org/WebSite/document/Report_CENCLCETSI_Standards_Smart%20Grids.pdf</p>
</div>
<div id="ftn45">
<h2><a name="h.xljjnb2jp8mo"></a> <a href="#_ftnref45" name="_ftn45"><sup><sup>[45]</sup></sup></a> SPRING Singapore Supported Close to 600 Companies in Standards Adoption, and Service Excellence Projects , 12th August 2015, http://www.spring.gov.sg/NewsEvents/PR/Pages/Internet-of-Things-(IoT)-Standards-Outline-to-Support-Smart-Nation-Initiative-Unveiled-20150812.aspx</h2>
</div>
</div>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards'>http://editors.cis-india.org/internet-governance/blog/database-on-big-data-and-smart-cities-international-standards</a>
</p>
No publishervanyaInternet GovernanceBig Data2016-02-11T15:49:45ZBlog EntryBig Data and Governance in India
http://editors.cis-india.org/internet-governance/events/big-data-governance-india
<b>The Centre for Internet & Society (CIS) is happy to invite you to a discussion on the role of Big Data in governance in India with a focus on Digital India, UID Scheme and Smart Cities Mission in India on January 23, 2016 at CIS office in Bangalore from 11 a.m. to 4 p.m.</b>
<h3><a href="http://editors.cis-india.org/internet-governance/blog/background-note-big-data" class="internal-link">Background Note</a></h3>
<hr />
<p>The roundtable discussion intends to delve deeper into various issues around the role of big data in Government schemes and projects like the Digital India, the UID Scheme and the 100 Smart Cities Mission. Some of the topics would include:</p>
<ul>
<li>Use/Assumptions about use of Big Data.</li>
<li>The public dialogue in the context of Big Data, rights, and governance.</li>
<li>Status and Role of India's data protection standards impacted by Big Data.</li>
<li>Legal hurdles posed by Big Data.</li>
</ul>
<p>We look forward to making this a forum for knowledge exchange and a learning opportunity for our friends and colleagues attending the discussion.</p>
<p><b>Contact:</b></p>
<ul>
<li>Vanya Rakesh vanya@cis-india.org +919586572707</li>
<li>Amber Sinha amber@cis-india.org +919620180343</li>
</ul>
<h2>Agenda</h2>
<table class="plain">
<tbody>
<tr>
<td>Introduction<br />11:00 am - 11.30 am<br /><br /></td>
<td>Introduction about “Big Data in the Global South: Mitigating Harms” and “Big Data in Indian Governance”.</td>
</tr>
<tr>
<td>Digital India<br />11.30 am - 1:00 pm<br /><br /></td>
<td>Discussion<br /><br />
<ul>
<li>Schemes under Digital India and how Big Data pertains to them</li>
</ul>
<ul>
<li>Scale and nature of data being collected</li>
</ul>
<ul>
<li>Actors involved</li>
</ul>
<ul>
<li>Research Methodology and coding</li>
</ul>
<ul>
<li>“Cradle to grave” identity</li>
</ul>
<ul>
<li>Need for privacy legislation/data protection policies</li>
</ul>
</td>
</tr>
<tr>
<td>1:00 pm- 2:00 pm <br /></td>
<td>Lunch</td>
</tr>
<tr>
<td>Big Data and Smart Cities<br />2:00 pm - 3:30pm <br /><br /></td>
<td>Discussion<br /><br />
<ul>
<li>Use/Assumptions about use of Big Data in Smart cities.</li>
</ul>
<ul>
<li>Organisations/companies driving the use of Big Data in Governance in India</li>
</ul>
<ul>
<li>The public dialogue around the scheme in the context of big data, rights, and governance</li>
</ul>
<ul>
<li>Impact of Big Data on India's Data Protection Standards </li>
</ul>
<ul>
<li>Impact of Big Data on other legislation/policy besides privacy . What type of 'legal hurdles' could Big Data pose?</li>
</ul>
<ul>
<li>Need for creating regulatory/legal framework</li>
</ul>
</td>
</tr>
<tr>
<td>3:30pm-4:00pm</td>
<td>Tea/Coffee</td>
</tr>
</tbody>
</table>
<ul>
</ul>
<h2>Detailed Agenda</h2>
<h3>Digital India</h3>
<p><b>Scope of schemes under Digital India and how Big Data pertains to them</b></p>
<ul>
<li>What are the ways in which Big Data is defined?</li>
<li>What aspects of Digital India initiatives pertain to Big Data?</li>
<li>What could be the harms/benefits of Big Data for Digital India?</li>
</ul>
<p><b>Scale and nature of data being collected</b></p>
<ul>
<li>What do the schemes intend to quantify?</li>
</ul>
<p><b>Actors involved</b></p>
<ul>
<li>What kinds of issue arise in PPP model?</li>
<li>Questions about ownership of data, access-control and security</li>
<li>Application of Section 43A rules to private parties involved</li>
</ul>
<p><b>Research Methodology and coding</b></p>
<ul>
<li>What the relevant questions that need to be asked in mapping each scheme?</li>
<li>How do we view e-governance initiatives vis-a-vis privacy principles?</li>
<li>What are the rights of citizens, and how are they impacted?</li>
</ul>
<p><b>“Cradle to grave” identity</b></p>
<ul>
<li>What does ‘cradle to grave’ digital identity mean?</li>
<li>What is the impact of using the Aadhaar number?</li>
</ul>
<p><b>Need for privacy legislation/data protection policies</b></p>
<ul>
<li>What aspects of the right to privacy pertain to the schemes?</li>
<li>Extending the Section 43A rules to government agencies</li>
<li>Justice Shah committee’s nine privacy principles.</li>
</ul>
<h3>Big Data and Smart Cities</h3>
<p><b>Use/Assumptions about use of Big Data in Smart cities</b></p>
<ul>
<li>What can be termed as big data in the context of smart cities.</li>
<li>What would be the role of big data.</li>
<li>Where do we see use/potential use of big data in the smart cities.</li>
</ul>
<p><b>What bodies/companies are driving the use of Big Data in Governance in India? </b></p>
<ul>
<li>Identifying actors involved.</li>
<li>Defining the role of: Government bodies, Private companies like IT Companies, consultants, etc. in use of big data. Clarity on ownership, storage, use, re-use, deletion of data. Question of accountability in case of breach/misuse.</li>
</ul>
<p><b>What has been the public dialogue around a scheme in the context of big data, rights, and governance? </b></p>
<ul>
<li>Weighing promises of big data.</li>
<li>Weighing challenges of big data.</li>
<li>Concerns around big data- data security, privacy, digital resilience of infrastructure, risks of identity management, Circumvention of democracy, social exclusion, right to equality, right to access, etc.</li>
<li>Issue of governance and implementation: role of SPVs.</li>
</ul>
<p><b>How are India's data protection standards impacted by Big Data? </b></p>
<ul>
<li>Need for developing standards.</li>
<li>Drawing from existing international standards.</li>
</ul>
<p><b>Are there other legislation/policy besides privacy impacted by Big Data? what type of 'legal hurdles' could Big Data pose?</b></p>
<ul>
<li>Legal landscaping: impact on current laws/policies/provisions.</li>
</ul>
<p><b>Need for creating regulatory/legal framework?</b></p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/events/big-data-governance-india'>http://editors.cis-india.org/internet-governance/events/big-data-governance-india</a>
</p>
No publisherpraskrishnaBig DataPrivacyInternet GovernanceSmart CitiesEvent2016-01-17T01:57:45ZEventBig Data in the Global South - An Analysis
http://editors.cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis
<b></b>
<h3 style="text-align: justify; "><b>I. </b> <b>Introduction</b></h3>
<p style="text-align: justify; ">"<i>The period that we have embarked upon is unprecedented in history in terms of our ability to learn about human behavior.</i>" <a href="#_ftn1" name="_ftnref1">[1]</a></p>
<p style="text-align: justify; ">The world we live in today is facing a slow but deliberate metamorphosis of decisive information; from the erstwhile monopoly of world leaders and the captains of industry obtained through regulated means, it has transformed into a relatively undervalued currency of knowledge collected from individual digital expressions over a vast network of interconnected electrical impulses.<a href="#_ftn2" name="_ftnref2">[2]</a> This seemingly random deluge of binary numbers, when interpreted represents an intricately woven tapestry of the choices that define everyday life, made over virtual platforms. The machines we once employed for menial tasks have become sensorial observers of our desires, wants and needs, so much so that they might now predict the course of our future choices and decisions.<a href="#_ftn3" name="_ftnref3">[3]</a> The patterns of human behaviour that are reflected within this data inform policy makers, in both a public and private context. The collective data obtained from our digital shadows thus forms a rapidly expanding storehouse of memory, from which interested parties can draw upon to resolve problems and enable a more efficient functioning of foundational institutions, such as the markets, the regulators and the government.<a href="#_ftn4" name="_ftnref4">[4]</a></p>
<p style="text-align: justify; ">The term used to describe a large volume of collected data, in a structured as well as unstructured form is called Big Data. This data requires niche technology, outside of traditional software databases, to process; simply because of its exponential increment in a relatively short period of time. Big Data is usually identified using a "three V" characterization - larger volume, greater variety and distinguishably high rates of velocity. <a href="#_ftn5" name="_ftnref5">[5]</a> This is exemplified in the diverse sources from which this data is obtained; mobile phone records, climate sensors, social media content, GPS satellite identifications and patterns of employment, to name a few. Big data analytics refers to the tools and methodologies that aim to transform large quantities of raw data into "interpretable data", in order to study and discern the same so that causal relationships between events can be conclusively established.<a href="#_ftn6" name="_ftnref6">[6]</a> Such analysis could allow for the encouragement of the positive effects of such data and a concentrated mitigation of negative outcomes.</p>
<p style="text-align: justify; ">This paper seeks to map out the practices of different governments, civil society, and the private sector with respect to the collection, interpretation and analysis of big data in the global south, illustrated across a background of significant events surrounding the use of big data in relevant contexts. This will be combined with an articulation of potential opportunities to use big data analytics within both the public and private spheres and an identification of the contextual challenges that may obstruct the efficient use of this data. The objective of this study is to deliberate upon how significant obstructions to the achievement of developmental goals within the global south can be overcome through an accurate recognition, interpretation and analysis of big data collected from diverse sources.<b> </b></p>
<h3 style="text-align: justify; "><b>II. </b> <b>Uses of Big Data in the Global Development</b></h3>
<p style="text-align: justify; ">Big Data for development is the process though which raw, unstructured and imperfect data is analyzed, interpreted and transformed into information that can be acted upon by governments and policy makers in various capacities. The amount of digital data available in the world today has grown from 150 exabytes in 2005 to 1200 exabytes in 2010.<a href="#_ftn7" name="_ftnref7">[7]</a> It is predicted that this figure would increase by 40% annually in the next few years<a href="#_ftn8" name="_ftnref8">[8]</a>, which is close to 40 times growth of the world's population. <a href="#_ftn9" name="_ftnref9">[9]</a> The implication of this is essentially that the share of available data in the world today that is less than a minute old is increasing at an exponential rate. Moreover, an increasing percentage of this data is produced and created real-time.</p>
<p style="text-align: justify; ">The data revolution that is incumbent upon us is characterized by a rapidly accumulating and continuously evolving stock of data prevalent` in both industrialized as well as developing countries. This data is extracted from technological services that act as sensors and reflect the behaviour of individuals in relation to their socio-economic circumstances.</p>
<p style="text-align: justify; ">For many global south countries, this data is generated through mobile phone technology. This trend is evident in Sub Saharan Africa, where mobile phone technology has been used as an effective substitute for often weak and unstructured State mechanisms such as faulty infrastructure, underdeveloped systems of banking and inferior telecommunication networks.<a href="#_ftn10" name="_ftnref10">[10]</a></p>
<p style="text-align: justify; ">For example, a recent study presented at the Data for Development session at the NetMob Conference at MIT used mobile phone data to analyze the impact of opening a new toll highway in Dakar, Senegal on human mobility, particularly how people commute to work in the metropolitan area. <a href="#_ftn11" name="_ftnref11"><sup><sup>[11]</sup></sup></a> A huge investment, the improved infrastructure is expected to result in a significant increase of people in and out of Dakar, along with the transport of essential goods. This would initiate rural development in the areas outside of Dakar and boost the value of land within the region.<a href="#_ftn12" name="_ftnref12"><sup><sup>[12]</sup></sup></a> The impact of the newly constructed highway can however only be analyzed effectively and accurately through the collection of this mobile phone data from actual commuters, on a real time basis.</p>
<p style="text-align: justify; ">Mobile phones technology is no longer used just for personal communication but has been transformed into an effective tool to secure employment opportunities, transfer money, determine stock options and assess the prices of various commodities.<a href="#_ftn13" name="_ftnref13">[13]</a> This generates vast amounts of data about individuals and their interactions with the government and private sector companies. Internet Traffic is predicted to grow between 25 to 30 % in the next few years in North America, Western Europe and Japan but in Latin America, The Middle East and Africa this figure has been expected to touch close to 50%.<a href="#_ftn14" name="_ftnref14">[14]</a> The bulk of this internet traffic can be traced back to mobile devices.</p>
<p style="text-align: justify; ">The potential applicability of Big Data for development at the most general level is the ability to provide an overview of the well being of a given population at a particular period of time.<a href="#_ftn15" name="_ftnref15">[15]</a> This overcomes the relatively longer time lag that is prevalent with most other traditional forms of data collection. The analysis of this data has helped, to a large extent, uncover "digital smoke signals" - or inherent changes in the usage patterns of technological services, by individuals within communities.<a href="#_ftn16" name="_ftnref16">[16]</a> This may act as an indicator of the changes in the underlying well-being of the community as a whole. This information about the well-being of a community derived from their usage of technology provides significantly relevant feedback to policy makers on the success or failure of particular schemes and can pin point changes that need to be made to status quo. <a href="#_ftn17" name="_ftnref17">[17]</a>The hope is that this feedback delivered in real-time, would in turn lead to a more flexible and accessible system of international development, thus securing more measurable and sustained outcomes. <a href="#_ftn18" name="_ftnref18">[18]</a></p>
<p style="text-align: justify; ">The analysis of big data involves the use of advanced computational technology that can aid in the determination of trends, patterns and correlations within unstructured data so as to transform it into actionable information. It is hoped that this in addition to the human perspective and experience afforded to the process could enable decision makers to rely upon information that is both reliable and up to date to formulate durable and self-sustaining development policies.</p>
<p style="text-align: justify; ">The availability of raw data has to be adequately complemented with intent and a capacity to use it effectively. To this effect, there is an emerging volume of literature that seeks to characterize the primary sources of this Big Data as sharing certain easily distinguishable features. Firstly, it is digitally generated and can be stored in a binary format, thus making it susceptible to requisite manipulation by computers attempting to engage in its interpretation. It is passively produced as a by-product of digital interaction and can be automatically extracted for the purpose of continuous analysis. It is also geographically traceable within a predetermined time period. It is however important to note that "real time" does not necessarily refer to information occurring instantly but is reflective of the relatively short time in which the information is produced and made available thus making it relevant within the requisite timeframe. This allows efficient responsive action to be taken in a short span of time thus creating a feedback loop. <a href="#_ftn19" name="_ftnref19">[19]</a></p>
<p style="text-align: justify; ">In most cases the granularity of the data is preferably sought to be expanded over a larger spatial context such as a village or a community as opposed to an individual simply because this affords an adequate recognition of privacy concerns and the lack of definitive consent of the individuals in the extraction of this data. In order to ease the process of determination of this data, the UN Global Pulse has developed taxonomy of sorts to assess the types of data sources that are relevant to utilizing this information for development purposes.<a href="#_ftn20" name="_ftnref20">[20]</a> These include the following sources;</p>
<p style="text-align: justify; "><i>Data Exhaust</i> or the digital footprint left behind by individuals' use of technology for service oriented tasks such as web purchases, mobile phone transactions and real time information collected by UN agencies to monitor their projects such as levels of food grains in storage units, attendance in schools etc.</p>
<p style="text-align: justify; "><i>Online Information</i> which includes user generated content on the internet such as news, blog entries and social media interactions which may be used to identify trends in human desires, perceptions and needs.</p>
<p style="text-align: justify; "><i>Physical sensors</i> such as satellite or infrared imagery of infrastructural development, traffic patterns, light emissions and topographical changes, thus enabling the remote sensing of changes in human activity over a period of time.</p>
<p style="text-align: justify; "><i>Citizen reporting or crowd sourced data</i> , which includes information produced on hotlines, mobile based surveys, customer generated maps etc. Although a passive source of data collection, this is a key instrument in assessing the efficacy of action oriented plans taken by decision makers.</p>
<p style="text-align: justify; ">The capacity to analyze this big data is hinged upon the reliance placed on technologically advanced processes such as powerful algorithms which can synthesize the abundance of raw data and break down the information enabling the identification of patterns and correlations. This process would rely on advanced visualization techniques such <i>"sense-making tools"<a href="#_ftn21" name="_ftnref21"><b>[21]</b></a></i></p>
<p style="text-align: justify; ">The identification of patterns within this data is carried out through a process of instituting a common framework for the analysis of this data. This requires the creation of a specific lexicon that would help tag and sort the collected data. This lexicon would specify <i>what </i>type of information is collected and <i>who </i>it is interpreted and collected by, the observer or the reporter. It would also aid in the determination of <i>how </i>the data is acquired and the qualitative and quantitative nature of the data. Finally, the spatial context of the data and the time frame within which it was collected constituting the aspects of <i>where </i>and <i>when</i> would be taken into consideration. The data would then be analyzed through a process of <i>Filtering, Summarizing and Categorizing</i> the data by transforming it into an appropriate collection of relevant indicators of a particular population demographic. <a href="#_ftn22" name="_ftnref22">[22]</a></p>
<p style="text-align: justify; ">The intensive mining of predominantly socioeconomic data is known as "reality mining" <a href="#_ftn23" name="_ftnref23">[23]</a> and this can shed light on the processes and interactions that are reflected within the data. This is carried out via a tested three fold process. Firstly, the " <i>Continuous Analysis over the streaming of the data", </i>which involves the monitoring and analyzing high frequency data streams to extract often uncertain raw data. For example, the systematic gathering of the prices of products sold online over a period of time. Secondly, <i>"The Online digestion of semi structured data and unstructured data", </i>which includes news articles, reviews of services and products and opinion polls on social media that aid in the determination of public perception, trends and contemporary events that are generating interest across the globe. Thirdly, a <i>'Real-time Correlation of streaming data with slowly accessible historical data repositories,' </i>which refers to the "mechanisms used for correlating and integrating data in real-time with historical records."<a href="#_ftn24" name="_ftnref24">[24]</a> The purpose of this stage is to derive a contextualized perception of personalized information that seeks to add value to the data by providing a historical context to it. <i> </i>Big Data for development purposes would make use of a combination of these depending on the context and need.<b> </b></p>
<p style="text-align: justify; "><b>(i) </b> <b>Policy Formulation </b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">The world today has become increasingly volatile in terms of how the decisions of certain countries are beginning to have an impact on vulnerable communities within entirely different nations. Our global economy has become infinitely more susceptible to fluctuating conditions primarily because of its interconnectivity hinged upon transnational interdependence. The primordial instigators of most of these changes, including the nature of harvests, prices of essential commodities, employment structures and capital flows, have been financial and environmental disruptions. <a href="#_ftn25" name="_ftnref25">[25]</a> According to the OECD, " <i> Disruptive shocks to the global economy are likely to become more frequent and cause greater economic and social hardship. The economic spillover effects of events like the financial crisis or a potential pandemic will grow due to the increasing interconnectivity of the global economy and the speed with which people, goods and data travel."<a href="#_ftn26" name="_ftnref26"><b>[26]</b></a> </i></p>
<p style="text-align: justify; ">The local impacts of these fluctuations may not be easily visible or even traceable but could very well be severe and long lasting. A vibrant literature on the vulnerability of communities has highlighted the impacts of these shocks on communities often causing children to drop out of school, families to sell their productive assets, and communities to place a greater reliance on state rations.<a href="#_ftn27" name="_ftnref27">[27]</a> These vulnerabilities cannot be definitively discerned through traditional systems of monitoring and information collection. The evidence of the effects of these shocks often take too long to reach decision makers; who are unable to formulate effective policies without ascertaining the nature and extent of the hardships suffered by these in a given context. The existing early warning systems in place do help raise flags and draw attention to the problem but their reach is limited and veracity compromised due to the time it takes to extract and collate this information through traditional means. These traditional systems of information collection are difficult to implement within rural impoverished areas and the data collected is not always reliable due to the significant time gap in its collection and subsequent interpretation. Data collected from surveys does provide an insight into the state of affairs of communities across demographics but this requires time to be collected, processed, verified and eventually published. Further, the expenses incurred in this process often prove to be difficult to offset.</p>
<p style="text-align: justify; "><b> The digital revolution therefore provides a significant opportunity to gain a richer and deeper insight into the very nature and evolution of the human experience itself thus affording a more legitimate platform upon which policy deliberations can be articulated. This data driven decision making, once the monopoly of private institutions such as The World Economic Forum and The McKinsey Institute <a href="#_ftn28" name="_ftnref28"><b>[28]</b></a> has now emerged at the forefront of the public policy discourse. Civil society has also expressed an eagerness to be more actively involved in the collection of real-time data after having perceived its benefits. This is evidenced by the emergence of 'crowd sourcing'<a href="#_ftn29" name="_ftnref29"><b>[29]</b></a> and other 'participatory sensing' <a href="#_ftn30" name="_ftnref30"><b>[30]</b></a> efforts that are founded upon the commonalities shared by like minded communities of individuals. This is being done on easily accessible platforms such as mobile phone interfaces, hand-held radio devices and geospatial technologies. <a href="#_ftn31" name="_ftnref31"><b>[31]</b></a> </b></p>
<p style="text-align: justify; ">The predictive nature of patterns identifiable from big data is extremely relevant for the purpose of developing socio-economic policies that seek to bridge problem-solution gaps and create a conducive environment for growth and development. Mobile phone technology has been able to quantify human behavior on an unprecedented scale.<a href="#_ftn32" name="_ftnref32">[32]</a> This includes being able to detect changes in standard commuting patterns of individuals based on their employment status<a href="#_ftn33" name="_ftnref33">[33]</a> and estimating a country's GDP in real-time by measuring the nature and extent of light emissions through remote sensing. <a href="#_ftn34" name="_ftnref34">[34]</a></p>
<p style="text-align: justify; ">A recent research study has concluded that "due to the relative frequency of certain queries being highly correlated with the percentage of physician visits in which individuals present influenza symptoms, it has been possible to accurately estimate the levels of influenza activity in each region of the United States, with a reporting lag of just a day." Online data has thus been used as a part of syndromic surveillance efforts also known as infodemiology. <a href="#_ftn35" name="_ftnref35">[35]</a> The US Centre for Disease Control has concluded that mining vast quantities of data through online health related queries can help detect disease outbreaks " <i> before they have been confirmed through a diagnosis or a laboratory confirmation." <a href="#_ftn36" name="_ftnref36"><b>[36]</b></a> </i> Google trends works in a similar way.</p>
<p style="text-align: justify; ">Another public health monitoring system known as the Healthmap project compiles seemingly fragmented data from news articles, social media, eye-witness reports and expert discussions based on validated studies to "<i>achieve a unified and comprehensive view of the current global state of infectious diseases"</i> that may be visualized on a map. <a href="#_ftn37" name="_ftnref37">[37]</a></p>
<p style="text-align: justify; ">Big Data used for development purpose can reduce the reliance on human inputs thus narrowing the room for error and ensuring the accuracy of information collected upon which policy makers can base their decisions.<b> </b></p>
<p style="text-align: justify; "><b>(ii) </b> <b>Advocacy and Social Change</b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Due to the ability of Big Data to provide an unprecedented depth of detail on particular issues, it has often been used as a vehicle of advocacy to highlight various issues in great detail. This makes it possible to ensure that citizens are provided with a far more participative experience, capturing their attention and hence better communicating these problems. Numerous websites have been able to use this method of crowd sourcing to broadcast socially relevant issues<a href="#_ftn38" name="_ftnref38">[38]</a>. Moreover, the massive increase in access to the internet has dramatically improved the scope for activism through the use of volunteered data due to which advocates can now collect data from volunteers more effectively and present these issues in various forums. Websites like Ushahidi<a href="#_ftn39" name="_ftnref39">[39]</a> and the Black Monday Movement <a href="#_ftn40" name="_ftnref40">[40]</a> being prime examples of the same. These platforms have championed various causes, consistently exposing significant social crises' that would otherwise go unnoticed.</p>
<p style="text-align: justify; ">The Ushahidi application used crowd sourcing mechanisms in the aftermath of the Haiti earthquake to set up a centralized messaging system that allowed mobile phone users to provide information on injured and trapped people.<a href="#_ftn41" name="_ftnref41">[41]</a> An analysis of the data showed that the concentration of text messages was correlated with the areas where there was an increased concentration of damaged buildings. <a href="#_ftn42" name="_ftnref42">[42]</a> Patrick Meier of Ushahidi noted "These results were evidence of the system's ability to predict, with surprising accuracy and statistical significance, the location and extent of structural damage post the earthquake." <a href="#_ftn43" name="_ftnref43">[43]</a></p>
<p style="text-align: justify; ">Another problem that data advocacy hopes to tackle, however, is that of too much exposure, with advocates providing information to various parties to help ensure that there exists no unwarranted digital surveillance and that sensitive advocacy tools and information are not used inappropriately. An interesting illustration of the same is The Tactical Technology Collective<a href="#_ftn44" name="_ftnref44">[44]</a> that hopes to improve the use of technology by activists and various other political actors. The organization, through various mediums such as films, events etc. hopes to train activists regarding data protection and privacy awareness and skills among human rights activists. Additionally, Tactical Technology also assists in ensuring that information is used in an appealing and relevant manner by human rights activists and in the field of capacity building for the purposes of data advocacy.</p>
<p style="text-align: justify; ">Observed data such as mobile phone records generated through network operators as well as through the use of social media are beginning to embody an omnipotent role in the development of academia through detailed research. This is due to the ability of this data to provide microcosms of information within both contexts of finer granularity and over larger public spaces. In the wake of natural disasters, this can be extremely useful, as reflected by the work of Flowminder after the 2010 Haiti earthquake.<a href="#_ftn45" name="_ftnref45">[45]</a> A similar string of interpretive analysis can be carried out in instances of conflict and crises over varying spans of time. Flowminder used the geospatial locations of 1.9 million subscriber identity modules in Haiti, beginning 42 days before the earthquake and 158 days after it. This information allowed researches to empirically determine the migration patterns of population post the earthquake and enabled a subsequent UNFPA household survey.<a href="#_ftn46" name="_ftnref46">[46]</a> In a similar capacity, the UN Global Pulse is seeking to assist in the process of consultation and deliberation on the specific targets of the millennium development goals through a framework of visual analytics that represent the big data procured on each of the topics proposed for the post- 2015 agenda online.<a href="#_ftn47" name="_ftnref47">[47]</a></p>
<p style="text-align: justify; ">A recent announcement of collaboration between RTI International, a non-profit research organization and IBM research lab looks promising in its initiative to utilize big data analytics in schools within Mombasa County, Kenya.<a href="#_ftn48" name="_ftnref48">[48]</a> The partnership seeks to develop testing systems that would capture data that would assist governments, non-profit organizations and private enterprises in making more informed decisions regarding the development of education and human resources within the region. Äs observed by Dr. Kamal Bhattacharya, The Vice President of IBM Research, "A significant lack of data on Africa in the past has led to misunderstandings regarding the history, economic performance and potential of the government." The project seeks to improve transparency and accountability within the schooling system in more than 100 institutions across the county. The teachers would be equipped with tablet devices to collate the data about students, classrooms and resources. This would allow an analysis of the correlation between the three aspects thus enabling better policy formulation and a more focused approach to bettering the school system. <a href="#_ftn49" name="_ftnref49">[49]</a> This is a part of the United States Agency for International Development's Education Data for Decision Making (EdData II) project. According to Dr Kommy Weldemariam, Research Scientist , IBM Research, "… there has been a significant struggle in making informed decisions as to how to invest in and improve the quality and content of education within Sub-Saharan Africa. The Project would create a school census hub which would enable the collection of accurate data regarding performance, attendance and resources at schools. This would provide valuable insight into the building of childhood development programs that would significantly impact the development of an efficient human capital pool in the near future."<a href="#_ftn50" name="_ftnref50">[50]</a></p>
<p style="text-align: justify; ">A similar initiative has been undertaken by Apple and IBM in the development of the "Student Achievement App" which seeks to use this data for "content analysis of student learning". The Application as a teaching tool that analyses the data provided to develop actionable intelligence on a per-student basis." <a href="#_ftn51" name="_ftnref51">[51]</a> This would give educators a deeper understanding of the outcome of teaching methodologies and subsequently enable better leaning. The impact of this would be a significant restructuring of how education is delivered. At a recent IBM sponsored workshop on education held in India last year , Katharine Frase, IBM CTO of Public Sector predicted that "classrooms will look significantly different within a decade than they have looked over the last 200 years."<a href="#_ftn52" name="_ftnref52">[52]</a><b> </b></p>
<p style="text-align: justify; "><b>(iii) </b> <b>Access and the exchange of information </b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Big data used for development serves as an important information intermediary that allows for the creation of a unified space within which unstructured heterogeneous data can be efficiently organized to create a collaborative system of information. New interactive platforms enable the process of information exchange though an internal vetting and curation that ensures accessibility to reliable and accurate information. This encourages active citizen participation in the articulation of demands from the government, thus enabling the actualization of the role of the electorate in determining specific policy decisions.</p>
<p style="text-align: justify; ">The Grameen Foundation's AppLab in Kampala aids in the development of tools that can use the information from micro financing transactions of clients to identify financial plans and instruments that would be be more suitable to their needs.<a href="#_ftn53" name="_ftnref53">[53]</a> Thus, through working within a community, this technology connects its clients in a web of information sharing that they both contribute to and access after the source of the information has been made anonymous. This allows the individual members of the community to benefit from this common pool of knowledge. The AppLab was able to identify the emergence of a new crop pest from an increase in online searches for an unusual string of search terms within a particular region. Using this as an early warning signal, the Grameen bank sent extension officers to the location to check the crops and the pest contamination was dealt with effectively before it could spread any further.<a href="#_ftn54" name="_ftnref54">[54]</a><b> </b></p>
<p style="text-align: justify; "><b>(iv) </b> <b>Accountability and Transparency</b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Big data enables participatory contributions from the electorate in existing functions such as budgeting and communication thus enabling connections between the citizens, the power brokers and elites. The extraction of information and increasing transparency around data networks is also integral to building a self-sustaining system of data collection and analysis. However it is important to note that this information collected must be duly analyzed in a responsible manner. Checking the veracity of the information collected and facilitating individual accountability would encourage more enthusiastic responses from the general populous thus creating a conducive environment to elicit the requisite information. The effectiveness of the policies formulated by relying on this information would rest on the accuracy of such information.</p>
<p style="text-align: justify; ">An example of this is Chequeado, a non-profit Argentinean media outlet that specializes in fact-checking. It works on a model of crowd sourcing information on the basis of which it has fact checked everything from the live presidential speech to congressional debates that have been made open to the public. <a href="#_ftn55" name="_ftnref55">[55]</a> It established a user friendly public database, DatoCHQ, in 2014 which allowed its followers to participate in live fact-checks by sending in data, which included references, facts, articles and questions, through twitter. <a href="#_ftn56" name="_ftnref56">[56]</a> This allowed citizens to corroborate the promises made by their leaders and instilled a sense of trust in the government.<b> </b></p>
<h3 style="text-align: justify; "><b>III. </b> <b>Big Data and Smart Cities in the Global South </b></h3>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Smart cities have become a buzzword in South Asia, especially after the Indian government led by Prime Minister Narendra Modi made a commitment to build 100 smart cities in India<a href="#_ftn57" name="_ftnref57">[57]</a>. A smart city is essentially designed as a hub where the information and communication technologies (ICT) are used to create feedback loops with an almost minimum time gap. In traditional contexts, surveys carried out through a state sponsored census were the only source of systematic data collection. However these surveys are long drawn out processes that often result in a drain on State resources. Additionally, the information obtained is not always accurate and policy makers are often hesitant to base their decisions on this information. The collection of data can however be extremely useful in improving the functionality of the city in terms of both the 'hard' or physical aspects of the infrastructural environment as well as the 'soft' services it provides to citizens. One model of enabling this data collection, to this effect, is a centrally structured framework of sensors that may be able to determine movements and behaviors in real-time, from which the data obtained can be subsequently analyzed. For example, sensors placed under parking spaces at intersections can relay such information in short spans of time. South Korea has managed to implement a similar structure within its smart city, Songdo.<a href="#_ftn58" name="_ftnref58">[58]</a></p>
<p style="text-align: justify; ">Another approach to this smart city model is using crowd sourced information through apps, either developed by volunteers or private conglomerates. These allow for the resolving of specific problems by organizing raw data into sets of information that are attuned to the needs of the public in a cohesive manner. However, this system would require a highly structured format of data sets, without which significantly transformational result would be difficult to achieve.<a href="#_ftn59" name="_ftnref59">[59]</a></p>
<p style="text-align: justify; ">There does however exist a middle ground, which allows the beneficiaries of this network, the citizens, to take on the role of primary sensors of information. This method is both cost effective and allows for an experimentation process within which an appropriate measure of the success or failure of the model would be discernible in a timely manner. It is especially relevant in fast growing cities that suffer congestion and breakdown of infrastructure due to the unprecedented population growth. This population is now afforded with the opportunity to become a part of the solution.</p>
<p style="text-align: justify; ">The principle challenge associated with extracting this Big Data is its restricted access. Most organizations that are able to collect this big data efficiently are private conglomerates and business enterprises, who use this data to give themselves a competitive edge in the market, by being able to efficiently identify the needs and wants of their clientele. These organizations are reluctant to release information and statistics because they fear it would result in them losing their competitive edge and they would consequently lose the opportunity to benefit monetarily from the data collected. Data leaks would also result in the company getting a bad name and its reputation could be significantly hampered. Despite the individual anonymity, the transaction costs incurred in ensuring the data of their individual customers is protected is often an expensive process. In addition to this there is a definite human capital gap resulting from the significant lack of scientists and analysts to interpret raw data transmitted across various channels.</p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; "><b>(i) </b> <b>Big Data in Urban Planning </b></p>
<p style="text-align: justify; ">Urban planning would require data that is reflective of the land use patterns of communities, combined with their travel descriptions and housing preferences. The mobility of individuals is dependent on their economic conditions and can be determined through an analysis of their purchases, either via online transactions or from the data accumulated by prominent stores. The primary source of this data is however mobile phones, which seemed to have transcend economic barriers. Secondary sources include cards used on public transport such as the Oyster card in London and the similar Octopus card used in Hong Kong. However, in most developing countries these cards are not available for public transport systems and therefore mobile network data forms the backbone of data analytics. An excessive reliance on the data collected through Smart phones could however be detrimental, especially in developing countries, simply because the usage itself would most likely be concentrated amongst more economically stable demographics and the findings from this data could potentially marginalize the poor.<a href="#_ftn60" name="_ftnref60">[60]</a></p>
<p style="text-align: justify; ">Mobile network big data (MNBD) is generated by all phones and includes CDRs, which are obtained from calls or texts that are sent or received, internet usage, topping up a prepaid value and VLR or Visitor Location Registry data which is generated whenever the phone is question has power. It essentially communicates to the Base Transceiver Stations (BSTs) that the phone is in the coverage area. The CDR includes records of calls made, duration of the call and information about the device. It is therefore stored for a longer period of time. The VLR data is however larger in volume and can be written over. Both VLR and CDR data can provide invaluable information that can be used for urban planning strategies. <a href="#_ftn61" name="_ftnref61">[61]</a> LIRNE<i>asia, </i>a regional policy and regulation think-tank has carried out an extensive study demonstrating the value of MNBD in SriLanka.<a href="#_ftn62" name="_ftnref62">[62]</a> This has been used to understand and sometimes even monitor land use patterns, travel patterns during peak and off seasons and the congregation of communities across regions. This study was however only undertaken after the data had been suitably pseudonymised.<a href="#_ftn63" name="_ftnref63">[63]</a> The study revealed that MNBD was incredibly valuable in generating important information that could be used by policy formulators and decision makers, because of two primary characteristics. Firstly, it comes close to a comprehensive coverage of the demographic within developing countries, thus using mobile phones as sensors to generate useful data. Secondly, people using mobile phones across vast geographic areas reflect important information regarding patterns of their travel and movement. <a href="#_ftn64" name="_ftnref64">[64]</a></p>
<p style="text-align: justify; ">MNBD allows for the tracking and mapping of changes in population densities on a daily basis, thus identifying 'home' and 'work' locations, informing policy makers of population congestion so that thy may be able to formulate policies with respect to easing this congestion. According to Rohan Samarajiva, founding chair of LIRNEasia, "This allows for real-time insights on the geo-spatial distribution of population, which may be used by urban planners to create more efficient traffic management systems."<a href="#_ftn65" name="_ftnref65"><sup><sup>[65]</sup></sup></a> This can also be used for the developmental economic policies. For example, the northern region of Colombo, a region inhabited by the low income families shows a lower population density on weekdays. This is reflective of the large numbers travelling to southern Colombo for employment. <a href="#_ftn66" name="_ftnref66"><sup><sup>[66]</sup></sup></a>Similarly, patterns of land use can be ascertained by analyzing the various loading patterns of base stations. Building on the success of the Mobile Data analysis project in SriLanka LIRNEasia plans to collaborate with partners in India and Bangladesh to assimilate real time information about the behavioral tendencies of citizens, using which policy makers may be able to make informed decisions. When this data is combined with user friendly virtual platforms such as smartphone Apps or web portals, it can also help citizens make informed choices about their day to day activities and potentially beneficial long term decisions. <a href="#_ftn67" name="_ftnref67"><sup><sup>[67]</sup></sup></a></p>
<p style="text-align: justify; "><b><i>Challenges of using Mobile Network Data</i></b></p>
<p style="text-align: justify; "><b><i> </i></b></p>
<p style="text-align: justify; ">Mobile networks invest significant sums of money in obtaining information regarding usage patterns of their services. Consequently, they may use this data to develop location based advertizing. In this context, there is a greater reluctance to share data for public purposes. Allowing access to one operator's big data by another could result in significant implications on the other with respect to the competitive advantage shared by the operator. A plausible solution to this conundrum is the accumulation of data from multiple sources without separating or organizing it according to the source it originates from. There is thus a lesser chance of sensitive information of one company being used by another. However, even operators do have concerns about how the data would be handled before this "mashing up" occurs and whether it might be leaked by the research organization itself. LIRNE<i>asia </i>used comprehensive non-disclosure agreements to ensure that the researchers who worked with the data were aware of the substantial financial penalties that may be imposed on them for data breaches. The access to the data was also restricted. <a href="#_ftn68" name="_ftnref68">[68]</a></p>
<p style="text-align: justify; ">Another line of argumentation advocates for the open sharing of data. A recent article in the <i>Economist </i>has articulated this in the context of the Ebola outbreak in West Africa. " <i> Releasing the data, though, is not just a matter for firms since people's privacy is involved. It requires governmental action as well. Regulators in each affected country would have to order operators to make their records accessible to selected researchers, who through legal agreements would only be allowed to use the data in a specific manner. For example, Orange, a major mobile phone network operator has made millions of CDRs from Senegal and The Ivory Coast available for researchers for their use under its Data Development Initiative. However the Political will amongst regulators and Network operators to do this seems to be lacking."<a href="#_ftn69" name="_ftnref69"><b>[69]</b></a> </i></p>
<p style="text-align: justify; ">It would therefore be beneficial for companies to collaborate with the customers who create the data and the researchers who want to use it to extract important insights. This however would require the creation of and subsequent adherence to self regulatory codes of conduct. <a href="#_ftn70" name="_ftnref70">[70]</a> In addition to this cooperation between network operators will assist in facilitating the transference of the data of their customers to research organizations. Sri Lanka is an outstanding example of this model of cooperation which has enabled various operators across spectrums to participate in the mobile-money enterprise.<a href="#_ftn71" name="_ftnref71">[71]</a><b> </b></p>
<p style="text-align: justify; "><b>(ii) </b> <b>Big Data and Government Delivery of Services and Functions </b></p>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">The analysis of Data procured in real time has proven to be integral to the formulation of policies, plans and executive decisions. Especially in an Asian context, Big data can be instrumental in urban development, planning and the allocation of resources in a manner that allows the government to keep up with the rapidly growing demands of an empowered population whose numbers are on an exponential rise. Researchers have been able to use data from mobile networks to engage in effective planning and management of infrastructure, services and resources. If, for example, a particular road or highway has been blocked for a particular period of time an alternative route is established before traffic can begin to build up creating a congestion, simply through an analysis of information collected from traffic lights, mobile networks and GPS systems.<a href="#_ftn72" name="_ftnref72">[72]</a></p>
<p style="text-align: justify; ">There is also an emerging trend of using big data for state controlled services such as the military. The South Korean Defense Minister Han Min Koo, in his recent briefing to President Park Geun-hye reflected on the importance of innovative technologies such as Big Data solutions. <a href="#_ftn73" name="_ftnref73">[73]</a></p>
<p style="text-align: justify; ">The Chinese government has expressed concerns regarding data breaches and information leakages that would be extremely dangerous given the exceeding reliance of governments on big data. A security report undertaken by Qihoo 360, China's largest software security provider established that 2,424 of the 17,875 Web security loopholes were on government websites. Considering the blurring line between government websites and external networks, it has become all the more essential for authorities to boost their cyber security protections.<a href="#_ftn74" name="_ftnref74">[74]</a></p>
<p style="text-align: justify; ">The Japanese government has considered investing resources in training more data scientists who may be able to analyze the raw data obtained from various sources and utilize requisite techniques to develop an accurate analysis. The Internal Affairs and Communication Ministry planned to launch a free online course on big data, the target of which would be corporate workers as well as government officials.<a href="#_ftn75" name="_ftnref75">[75]</a></p>
<p style="text-align: justify; ">Data analytics is emerging as an efficient technique of monitoring the public transport management systems within Singapore. A recent collaboration between IBM, StarHub, The Land Transport Authority and SMRT initiated a research study to observe the movement of commuters across regions. <a href="#_ftn76" name="_ftnref76"><sup><sup>[76]</sup></sup></a> This has been instrumental in revamping the data collection systems already in place and has allowed for the procurement of additional systems of monitoring.<a href="#_ftn77" name="_ftnref77"><sup><sup>[77]</sup></sup></a> The idea is essentially to institute a "black box" of information for every operational unit that allows for the relaying of real-time information from sources as varied as power switches, tunnel sensors and the wheels, through assessing patterns of noise and vibration. <a href="#_ftn78" name="_ftnref78"><sup><sup>[78]</sup></sup></a></p>
<p style="text-align: justify; ">In addition to this there are numerous projects in place that seek to utilize Big Data to improve city life. According to Carlo Ritti, Director of the MIT Senseable City Lab, "We are now able to analyze the pulse of a city from moment to moment. Over the past decade, digital technologies have begun to blanket our cities, forming the backbone of a large, intelligent infrastructure." <a href="#_ftn79" name="_ftnref79"><sup><sup>[79]</sup></sup></a> The professor of Information Architecture and Founding Director of the Singapore ETH Centre, Gerhart Schmitt has observed that "the local weather has a major impact on the behavior of a population." In this respect the centre is engaged in developing a range of visual platforms to inform citizens on factors such as air quality which would enable individuals to make everyday choices such as what route to take when planning a walk or predict a traffic jam. <a href="#_ftn80" name="_ftnref80"><sup><sup>[80]</sup></sup></a> Schmitt's team has also been able to arrive at a pattern that connects the demand for taxis with the city's climate. The amalgamation of taxi location with rainfall data has been able to help locals hail taxis during a storm. This form of data can be used in multiple ways allowing the visualization of temperature hotspots based on a "heat island" effect where buildings, cars and cooling units cause a rise in temperature. <a href="#_ftn81" name="_ftnref81"><sup><sup>[81]</sup></sup></a></p>
<p style="text-align: justify; ">Microsoft has recently entered into a partnership with the Federal University of Minas Gerais, one of the largest universities in Brazil to undertake a research project that could potentially predict traffic jams up to an hour in advance. <a href="#_ftn82" name="_ftnref82"><sup><sup>[82]</sup></sup></a> The project attempts to analyze information from transport departments, road traffic cameras and drivers social network profiles to identify patterns that they could use to help predict traffic jams approximately 15 to 60 minutes before they actually happen.<a href="#_ftn83" name="_ftnref83"><sup><sup>[83]</sup></sup></a></p>
<p style="text-align: justify; ">In anticipation of the increasing demand for professionals with requisite training in data sciences, the Malaysian Government has planned to increase the number of local data scientists from the present 80 to 1500 by 2020, through the support of the universities within the country.<b> </b></p>
<h3 style="text-align: justify; "><b>IV. </b> <b>Big Data and the Private Sector in the Global South </b></h3>
<h2 style="text-align: justify; "></h2>
<p style="text-align: justify; ">Essential considerations in the operations of Big Data in the Private sector in the Asia Pacific region have been extracted by a comprehensive survey carried out by the Economist Intelligence Unit.<a href="#_ftn84" name="_ftnref84">[84]</a> Over 500 executives across the Asia Pacific region were surveyed, from across industries representing a diverse range of functions. 69% of these companies had an annual turnover of over US $500m. The respondents were senior managers responsible for taking key decisions with regard to investment strategies and the utilization of big data for the same.</p>
<p style="text-align: justify; ">The results of the Survey conclusively determined that firms in the Asia Pacific region have had limited success with implementing Big Data Practices. A third of the respondents claimed to have an advanced knowledge of the utilization of big data while more than half claim to have made limited progress in this regard. Only 9% of the Firms surveyed cited internal barriers to implementing big data practices. This included a significant difficulty in enabling the sharing of information across boundaries. Approximately 40% of the respondents surveyed claimed they were unaware of big data strategies, even if they had in fact been in place simply because these had been poorly communicated to them. Almost half of the firms however believed that big data plays an important role in the success of the firm and that it can contribute to increasing revenue by 25% or more.</p>
<p style="text-align: justify; ">Numerous obstacles in the adoption of big data were cited by the respondents. These include the lack of suitable software to interpret the data and the lack of in-house skills to analyze the data appropriately. In addition to this, the lack of willingness on the part of various departments to share their data for the fear of a breach or leak was thought to be a major hindrance. This combined with a lack of communication between the various departments and exceedingly complicated reports that cannot be analyzed given the limited resources and lack of human capital qualified enough to carry out such an analysis, has resulted in an indefinite postponement of any policy propounding the adoption of big data practices.</p>
<p style="text-align: justify; ">Over 59% of the firms surveyed agreed that collaboration is integral to innovation and that information silos are a huge hindrance within a knowledge based economy. There is also a direct correlation between the size of the company and its progress in adopting big data, with larger firms adopting comprehensive strategies more frequently than smaller ones. A major reason for this is that large firms with substantially greater resources are able to actualize the benefits of big data analytics more efficiently than firms with smaller revenues. These businesses which have advanced policies in place outlining their strategies with respect to their reliance on big data are also more likely to communicate these strategies to their employees to ensure greater clarity in the process.</p>
<p style="text-align: justify; ">The use of big data was recently voted as the "best management practice" of the past year according to a cumulative ranking published by Chief Executive China Magazine, a Trade journal published by Global Sources on 13th January, 2015 in Beijing. The major benefit cited was the real-time information sourced from customers, which allows for direct feedback from clients when making decisions regarding changes in products or services. <a href="#_ftn85" name="_ftnref85">[85]</a></p>
<p style="text-align: justify; ">A significant contributor to the lack of adequate usage of data analytics is the belief that a PhD is a prerequisite for entering the field of data science. This misconception was pointed out by Richard Jones, vice president of Cloudera in the Australia, New Zealand and the Asean region. Cloudera provides businesses with the requisite professional services that they may need to effectively utilize Big Data. This includes a combination of the necessary manpower, technology and consultancy services.<a href="#_ftn86" name="_ftnref86"><sup><sup>[86]</sup></sup></a> Deepak Ramanathan, the chief technology officer, SAS Asia Pacific believes that this skill gap can be addressed by forming data science teams within both governments and private enterprises. These teams could comprise of members with statistical, coding and business skills and allow them to work in a collaborative manner to address the problem at hand.<a href="#_ftn87" name="_ftnref87"><sup><sup>[87]</sup></sup></a> SAS is an Enterprise Software Giant that creates tools tailored to suit business users to help them interpret big data. Eddie Toh, the planning and marketing manager of Intel's data center platform believes that businesses do not necessarily need data scientists to be able to use big data analytics to their benefit and can in fact outsource the technical aspects of the interpretation of this data as and when required.<a href="#_ftn88" name="_ftnref88"><sup><sup>[88]</sup></sup></a></p>
<p style="text-align: justify; ">The analytical team at Dell has forged a partnership with Brazilian Public Universities to facilitate the development of a local talent pool in the field of data analytics. The Instituto of Data Science (IDS) will provide training methodologies for in person or web based classes. <a href="#_ftn89" name="_ftnref89"><sup><sup>[89]</sup></sup></a> The project is being undertaken by StatSoft, a subsidiary of Dell that was acquired by the technology giant last year. <a href="#_ftn90" name="_ftnref90"><sup><sup>[90]</sup></sup></a></p>
<h3 style="text-align: justify; "><b>V. </b> <b>Conclusion</b></h3>
<p style="text-align: justify; "><b> </b></p>
<p style="text-align: justify; ">There have emerged numerous challenges in the analysis and interpretation of Big Data. While it presents an extremely engaging opportunity, which has the potential to transform the lives of millions of individuals, inform the private sector and influence government, the actualization of this potential requires the creation of a sustainable foundational framework ; one that is able to mitigate the various challenges that present themselves in this context.</p>
<p style="text-align: justify; ">A colossal increase in the rate of digitization has resulted in an unprecedented increment in the amount of Big Data available, especially through the rapid diffusion cellular technology. The importance of mobile phones as a significant source of data, especially in low income demographics cannot be overstated. This can be used to understand the needs and behaviors of large populations, providing an in depth insight into the relevant context within which valuable assessments as to the competencies, suitability and feasibilities of various policy mechanisms and legal instruments can be made. However, this explosion of data does have a lasting impact on how individuals and organizations interact with each other, which might not always be reflected in the interpretation of raw data without a contextual understanding of the demographic. It is therefore vital to employ the appropriate expertise in assessing and interpreting this data. The significant lack of a human resource to capital to analyze this information in an accurate manner poses a definite challenge to its effective utilization in the Global South.</p>
<p style="text-align: justify; ">The legal and technological implications of using Big Data are best conceptualized within the deliberations on protecting the privacy of the contributors to this data. The primary producers of this information, from across platforms, are often unaware that they are in fact consenting to the subsequent use of the data for purposes other than what was intended. For example people routinely accept terms and conditions of popular applications without understanding where or how the data that they inadvertently provide will be used.<a href="#_ftn91" name="_ftnref91">[91]</a> This is especially true of media generated on social networks that are increasingly being made available on more accessible platforms such as mobile phones and tablets. Privacy has and always will remain an integral pillar of democracy. It is therefore essential that policy makers and legislators respond effectively to possible compromises of privacy in the collection and interpretation of this data through the institution of adequate safeguards in this respect.</p>
<p style="text-align: justify; ">Another challenge that has emerged is the access and sharing of this data. Private corporations have been reluctant to share this data due to concerns about potential competitors being able to access and utilize the same. In addition to this, legal considerations also prevent the sharing of data collected from their customers or users of their services. The various technical challenges in storing and interpreting this data adequately also prove to be significant impediments in the collection of data. It is therefore important that adequate legal agreements be formulated in order to facilitate a reliable access to streams of data as well as access to data storage facilities to accommodate for retrospective analysis and interpretation.</p>
<p style="text-align: justify; ">In order for the use of Big Data to gain traction, it is important that these challenges are addressed in an efficient manner with durable and self-sustaining mechanisms of resolving significant obstructions. The debates and deliberations shaping the articulation of privacy concerns and access to such data must be supported with adequate tools and mechanisms to ensure a system of <i>"privacy-preserving analysis." The </i>UN Global Pulse has put forth the concept of data philanthropy to attempt to resolve these issues, wherein " <i>corporations </i>[would] <i> take the initiative to anonymize (strip out all personal information) their data sets and provide this data to social innovators to mine the data for insights, patterns and trends in realtime or near realtime."<a href="#_ftn92" name="_ftnref92"><b>[92]</b></a> </i></p>
<p style="text-align: justify; "><i> </i></p>
<p style="text-align: justify; ">The concept of data philanthropy highlights particular challenges and avenues that may be considered for future deliberations that may result in specific refinements to the process.</p>
<p style="text-align: justify; ">One of the primary uses of Big Data, especially in developing countries is to address important developmental issues such as the availability of clean water, food security, human health and the conservation of natural resources. Effective Disaster management has also emerged as one of the key functions of Big Data. It therefore becomes all the more important for organizations to assess the information supply chains pertaining to specific data sources in order to identify and prioritize the issues of data management. <a href="#_ftn93" name="_ftnref93">[93]</a> Data emerging from different contexts, across different sources may appear in varied compositions and would differ significantly across economic demographics. The Big Data generated from certain contexts would be inefficient due to the unavailability of data within certain regions and the resulting studies affecting policy decisions should take into account this discrepancy. This data unavailability has resulted in a digital divide which is especially prevalent in the global south. <a href="#_ftn94" name="_ftnref94">[94]</a></p>
<p style="text-align: justify; ">Appropriate analysis of the Big Data generated would provide a valuable insight into the key areas and inform policy makers with respect to important decisions. However, it is necessary to ensure that the quality of this data meets a specific standard and appropriate methodological processes have been undertaken to interpret and analyze this data. The government is a key actor that can shape the ecosystem surrounding the generation, analysis and interpretation of big data. It is therefore essential that governments of countries across the global south recognize the need to collaborate with civic organizations as well technical experts in order to create appropriate legal frameworks for the effective utilization of this data.</p>
<div style="text-align: justify; ">
<hr />
<div id="ftn1">
<p><a href="#_ftnref1" name="_ftn1">[1]</a> Onella, Jukka- Pekka. <i>"</i>Social Networks and Collective Human Behavior<i>." UN Global Pulse</i>. 10 Nov.2011. <http://www.unglobalpulse.org/node/14539></p>
</div>
<div id="ftn2">
<p><a href="#_ftnref2" name="_ftn2">[2]</a> http://www.business2community.com/big-data/evaluating-big-data-predictive-analytics-01277835</p>
</div>
<div id="ftn3">
<p><a href="#_ftnref3" name="_ftn3">[3]</a> Ibid</p>
</div>
<div id="ftn4">
<p><a href="#_ftnref4" name="_ftn4">[4]</a> http://unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf</p>
</div>
<div id="ftn5">
<p><a href="#_ftnref5" name="_ftn5">[5]</a> Ibid, p.13, pp.5</p>
</div>
<div id="ftn6">
<p><a href="#_ftnref6" name="_ftn6">[6]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn7">
<p><a href="#_ftnref7" name="_ftn7">[7]</a> Helbing, Dirk , and Stefano Balietti. "From Social Data Mining to Forecasting Socio-Economic Crises." <i>Arxiv </i>(2011) 1-66. 26 Jul 2011 http://arxiv.org/pdf/1012.0178v5.pdf.</p>
</div>
<div id="ftn8">
<p><a href="#_ftnref8" name="_ftn8">[8]</a> Manyika, James, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh andAngela H. Byers. <i>"</i>Big data: The next frontier for innovation, competition, and productivity.<i>" McKinsey</i></p>
<p><i>Global Institute </i> (2011): 1-137. May 2011.</p>
</div>
<div id="ftn9">
<p><a href="#_ftnref9" name="_ftn9">[9]</a> "World Population Prospects, the 2010 Revision." <i>United Nations Development Programme.</i> <http://esa.un.org/unpd/wpp/unpp/panel_population.htm></p>
</div>
<div id="ftn10">
<p><a href="#_ftnref10" name="_ftn10">[10]</a> Mobile phone penetration, measured by Google, from the number of mobile phones per 100 habitants, was 96% in Botswana, 63% in Ghana, 66% in Mauritania, 49% in Kenya, 47% in Nigeria, 44% in Angola, 40% in Tanzania (Source: Google Fusion Tables)</p>
</div>
<div id="ftn11">
<p><a href="#_ftnref11" name="_ftn11">[11]</a> http://www.brookings.edu/blogs/africa-in-focus/posts/2015/04/23-big-data-mobile-phone-highway-sy</p>
</div>
<div id="ftn12">
<p><a href="#_ftnref12" name="_ftn12">[12]</a> Ibid</p>
</div>
<div id="ftn13">
<p><a href="#_ftnref13" name="_ftn13">[13]</a> <http://www.google.com/fusiontables/Home/></p>
</div>
<div id="ftn14">
<p><a href="#_ftnref14" name="_ftn14">[14]</a> "Global Internet Usage by 2015 [Infographic]." <i>Alltop. </i><http://holykaw.alltop.com/global-internetusage-by-2015-infographic?tu3=1></p>
</div>
<div id="ftn15">
<p><a href="#_ftnref15" name="_ftn15">[15]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011 <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn16">
<p><a href="#_ftnref16" name="_ftn16">[16]</a> Ibid</p>
</div>
<div id="ftn17">
<p><a href="#_ftnref17" name="_ftn17">[17]</a> Ibid</p>
</div>
<div id="ftn18">
<p><a href="#_ftnref18" name="_ftn18">[18]</a> Ibid</p>
</div>
<div id="ftn19">
<p><a href="#_ftnref19" name="_ftn19">[19]</a> Goetz, Thomas. "Harnessing the Power of Feedback Loops." <i>Wired.com. </i>Conde Nast Digital, 19 June 2011. <http://www.wired.com/magazine/2011/06/ff_feedbackloop/all/1>.</p>
</div>
<div id="ftn20">
<p><a href="#_ftnref20" name="_ftn20">[20]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn21">
<p><a href="#_ftnref21" name="_ftn21">[21]</a> Bollier, David. <i>The Promise and Peril of Big Data. </i>The Aspen Institute, 2010. <http://www.aspeninstitute.org/publications/promise-peril-big-data></p>
</div>
<div id="ftn22">
<p><a href="#_ftnref22" name="_ftn22">[22]</a> Ibid</p>
</div>
<div id="ftn23">
<p><a href="#_ftnref23" name="_ftn23">[23]</a> Eagle, Nathan and Alex (Sandy) Pentland. "Reality Mining: Sensing Complex Social Systems",<i>Personal and Ubiquitous Computing</i>, 10.4 (2006): 255-268.</p>
</div>
<div id="ftn24">
<p><a href="#_ftnref24" name="_ftn24">[24]</a> Kirkpatrick, Robert. "Digital Smoke Signals." <i>UN Global Pulse. </i>21 Apr. 2011. <http://www.unglobalpulse.org/blog/digital-smoke-signals></p>
</div>
<div id="ftn25">
<p><a href="#_ftnref25" name="_ftn25">[25]</a> OECD, Future Global Shocks, Improving Risk Governance, 2011</p>
</div>
<div id="ftn26">
<p><a href="#_ftnref26" name="_ftn26">[26]</a> "Economy: Global Shocks to Become More Frequent, Says OECD." <i>Organisation for Economic Cooperationand Development. </i>27 June. 2011.</p>
</div>
<div id="ftn27">
<p><a href="#_ftnref27" name="_ftn27">[27]</a> Friedman, Jed, and Norbert Schady. <i>How Many More Infants Are Likely to Die in Africa as a Result of the Global Financial Crisis? </i>Rep. The World Bank <http://siteresources.worldbank.org/INTAFRICA/Resources/AfricaIMR_FriedmanSchady_060209.pdf></p>
</div>
<div id="ftn28">
<p><a href="#_ftnref28" name="_ftn28">[28]</a> Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute,June 2011<http://www.mckinsey.com/mgi/publications/big_data/pdfs/MGI_big_data_full_report.pdf></p>
</div>
<div id="ftn29">
<p><a href="#_ftnref29" name="_ftn29">[29]</a> The word "crowdsourcing" refers to the use of non-official actors ("the crowd") as (free) sources of information, knowledge and services, in reference and opposition to the commercial practice of</p>
<p>outsourcing. "</p>
</div>
<div id="ftn30">
<p><a href="#_ftnref30" name="_ftn30">[30]</a> Burke, J., D. Estrin, M. Hansen, A. Parker, N. Ramanthan, S. Reddy and M.B. Srivastava. <i>ParticipatorySensing. </i>Rep. Escholarship, University of California, 2006. <http://escholarship.org/uc/item/19h777qd>.</p>
</div>
<div id="ftn31">
<p><a href="#_ftnref31" name="_ftn31">[31]</a> "Crisis Mappers Net-The international Network of Crisis Mappers." <http://crisismappers.net>, http://haiti.ushahidi.com and Goldman et al., 2009</p>
</div>
<div id="ftn32">
<p><a href="#_ftnref32" name="_ftn32">[32]</a> Alex Pentland cited in "When There's No Such Thing As Too Much Information". <i>The New York Times</i>.23 Apr. 2011<http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.</p>
</div>
<div id="ftn33">
<p><a href="#_ftnref33" name="_ftn33">[33]</a> Nathan Eagle also cited in "When There's No Such Thing As Too Much Information". <i>The New YorkTimes</i>. 23 Apr. 2011. <http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=1&src=tptw>.</p>
</div>
<div id="ftn34">
<p><a href="#_ftnref34" name="_ftn34">[34]</a> Helbing and Balietti. "From Social Data Mining to Forecasting Socio-Economic Crisis."</p>
</div>
<div id="ftn35">
<p><a href="#_ftnref35" name="_ftn35">[35]</a> Eysenbach G. <i>Infodemiology: tracking flu-related searches on the Web for syndromic surveillance.</i>AMIA (2006)<http://yi.com/home/EysenbachGunther/publications/2006/eysenbach2006cinfodemiologyamia proc.pdf></p>
</div>
<div id="ftn36">
<p><a href="#_ftnref36" name="_ftn36">[36]</a> Syndromic Surveillance (SS)." <i>Centers for Disease Control and Prevention. </i>06 Mar. 2012.<http://www.cdc.gov/ehrmeaningfuluse/Syndromic.html>.</p>
</div>
<div id="ftn37">
<p><a href="#_ftnref37" name="_ftn37">[37]</a> Health Map <http://healthmap.org/en/></p>
</div>
<div id="ftn38">
<p><a href="#_ftnref38" name="_ftn38">[38]</a> see <a href="http://www.detective.io/">www.detective.io</a></p>
</div>
<div id="ftn39">
<p><a href="#_ftnref39" name="_ftn39">[39]</a> www.ushahidi.com</p>
</div>
<div id="ftn40">
<p><a href="#_ftnref40" name="_ftn40">[40]</a> <a href="http://www.facebook.com/BlackMondayMovement">www.facebook.com/BlackMondayMovement</a></p>
</div>
<div id="ftn41">
<p><a href="#_ftnref41" name="_ftn41">[41]</a> Ushahidi is a nonprofit tech company that was developed to map reports of violence in Kenya followingthe 2007 post-election fallout. Ushahidi specializes in developing "<i>free and open source software for</i></p>
<p><i>information collection, visualization and interactive mapping." </i> <http://ushahidi.com></p>
</div>
<div id="ftn42">
<p><a href="#_ftnref42" name="_ftn42">[42]</a> Conducted by the European Commission's Joint Research Center against data on damaged buildingscollected by the World Bank and the UN from satellite images through spatial statistical techniques.</p>
</div>
<div id="ftn43">
<p><a href="#_ftnref43" name="_ftn43">[43]</a> www.ushahidi.com</p>
</div>
<div id="ftn44">
<p><a href="#_ftnref44" name="_ftn44">[44]</a> See https://<b>tacticaltech</b>.org/</p>
</div>
<div id="ftn45">
<p><a href="#_ftnref45" name="_ftn45">[45]</a> see www. flowminder.org</p>
</div>
<div id="ftn46">
<p><a href="#_ftnref46" name="_ftn46">[46]</a> Ibid</p>
</div>
<div id="ftn47">
<p><a href="#_ftnref47" name="_ftn47">[47]</a> <a href="http://post2015.unglobalpulse.net/">http://post2015.unglobalpulse.net/</a></p>
</div>
<div id="ftn48">
<p><a href="#_ftnref48" name="_ftn48">[48]</a> http://allafrica.com/stories/201507151726.html</p>
</div>
<div id="ftn49">
<p><a href="#_ftnref49" name="_ftn49">[49]</a> Ibid</p>
</div>
<div id="ftn50">
<p><a href="#_ftnref50" name="_ftn50">[50]</a> Ibid</p>
</div>
<div id="ftn51">
<p><a href="#_ftnref51" name="_ftn51">[51]</a> http://www.computerworld.com/article/2948226/big-data/opinion-apple-and-ibm-have-big-data-plans-for-education.html</p>
</div>
<div id="ftn52">
<p><a href="#_ftnref52" name="_ftn52">[52]</a> Ibid</p>
</div>
<div id="ftn53">
<p><a href="#_ftnref53" name="_ftn53">[53]</a> http://www.grameenfoundation.org/where-we-work/sub-saharan-africa/uganda</p>
</div>
<div id="ftn54">
<p><a href="#_ftnref54" name="_ftn54">[54]</a> Ibid</p>
</div>
<div id="ftn55">
<p><a href="#_ftnref55" name="_ftn55">[55]</a> http://chequeado.com/</p>
</div>
<div id="ftn56">
<p><a href="#_ftnref56" name="_ftn56">[56]</a> http://datochq.chequeado.com/</p>
</div>
<div id="ftn57">
<p><a href="#_ftnref57" name="_ftn57">[57]</a> <i>Times of India </i> (2015): "Chandigarh May Become India's First Smart City," 12 January, http://timesofi ndia.indiatimes.com/india/Chandigarh- may-become-Indias-fi rst-smart-city/articleshow/ 45857738.cms</p>
</div>
<div id="ftn58">
<p><a href="#_ftnref58" name="_ftn58">[58]</a> http://www.cisco.com/web/strategy/docs/scc/ioe_citizen_svcs_white_paper_idc_2013.pdf</p>
</div>
<div id="ftn59">
<p><a href="#_ftnref59" name="_ftn59">[59]</a> Townsend, Anthony M (2013): <i>Smart Cities: Big Data, Civic Hackers and the Quest for a New Utopia</i>, New York: WW Norton.</p>
</div>
<div id="ftn60">
<p><a href="#_ftnref60" name="_ftn60">[60]</a> See "Street Bump: Help Improve Your Streets" on Boston's mobile app to collect data on roadconditions, <a href="http://www.cityofboston.gov/DoIT/">http://www.cityofboston.gov/DoIT/</a> apps/streetbump.asp</p>
</div>
<div id="ftn61">
<p><a href="#_ftnref61" name="_ftn61">[61]</a> Mayer-Schonberger, V and K Cukier (2013): <i>Big Data: A Revolution That Will Transform How We Live, Work, and Think</i>, London: John Murray.</p>
</div>
<div id="ftn62">
<p><a href="#_ftnref62" name="_ftn62">[62]</a> http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html</p>
</div>
<div id="ftn63">
<p><a href="#_ftnref63" name="_ftn63">[63]</a> Ibid</p>
</div>
<div id="ftn64">
<p><a href="#_ftnref64" name="_ftn64">[64]</a> Newman, M E J and M Girvan (2004): "Finding and Evaluating Community Structure in Networks,"<i>Physical Review E, American Physical Society</i>, Vol 69, No 2.</p>
</div>
<div id="ftn65">
<p><a href="#_ftnref65" name="_ftn65">[65]</a> http://www.sundaytimes.lk/150412/sunday-times-2/big-data-can-make-south-asian-cities-smarter-144237.html</p>
</div>
<div id="ftn66">
<p><a href="#_ftnref66" name="_ftn66">[66]</a> Ibid</p>
</div>
<div id="ftn67">
<p><a href="#_ftnref67" name="_ftn67">[67]</a> Ibid</p>
</div>
<div id="ftn68">
<p><a href="#_ftnref68" name="_ftn68">[68]</a> http://www.epw.in/review-urban-affairs/big-data-improve-urban-planning.html</p>
</div>
<div id="ftn69">
<p><a href="#_ftnref69" name="_ftn69">[69]</a> GSMA (2014): "GSMA Guidelines on Use of Mobile Data for Responding to Ebola," October, http:// <a href="http://www.gsma.com/mobilefordevelopment/wpcontent/">www.gsma.com/mobilefordevelopment/wpcontent/</a> uploads/2014/11/GSMA-Guidelineson-</p>
<p>protecting-privacy-in-the-use-of-mobilephone- data-for-responding-to-the-Ebola-outbreak-_ October-2014.pdf</p>
</div>
<div id="ftn70">
<p><a href="#_ftnref70" name="_ftn70">[70]</a> An example of the early-stage development of a self-regulatory code may be found at http:// lirneasia.net/2014/08/what-does-big-data-sayabout- sri-lanka/</p>
</div>
<div id="ftn71">
<p><a href="#_ftnref71" name="_ftn71">[71]</a> See "Sri Lanka's Mobile Money Collaboration Recognized at MWC 2015," <a href="http://lirneasia/">http://lirneasia</a>. net/2015/03/sri-lankas-mobile-money-colloboration- recognized-at-mwc-2015/</p>
</div>
<div id="ftn72">
<p><a href="#_ftnref72" name="_ftn72">[72]</a> http://www.thedailystar.net/big-data-for-urban-planning-57593</p>
</div>
<div id="ftn73">
<p><a href="#_ftnref73" name="_ftn73">[73]</a> <a href="http://koreaherald.com/">http://koreaherald.com</a> , 19/01/2015</p>
</div>
<div id="ftn74">
<p><a href="#_ftnref74" name="_ftn74">[74]</a> http://www.news.cn/, 25/11/2014</p>
</div>
<div id="ftn75">
<p><a href="#_ftnref75" name="_ftn75">[75]</a> <a href="http://the-japan-news.com/">http://the-japan-news.com</a> , 20/01/2015</p>
</div>
<div id="ftn76">
<p><a href="#_ftnref76" name="_ftn76">[76]</a> http://www.todayonline.com/singapore/can-big-data-help-tackle-mrt-woes</p>
</div>
<div id="ftn77">
<p><a href="#_ftnref77" name="_ftn77">[77]</a> Ibid</p>
</div>
<div id="ftn78">
<p><a href="#_ftnref78" name="_ftn78">[78]</a> Ibid</p>
</div>
<div id="ftn79">
<p><a href="#_ftnref79" name="_ftn79">[79]</a> http://edition.cnn.com/2015/06/24/tech/big-data-urban-life-singapore/</p>
</div>
<div id="ftn80">
<p><a href="#_ftnref80" name="_ftn80">[80]</a> Ibid</p>
</div>
<div id="ftn81">
<p><a href="#_ftnref81" name="_ftn81">[81]</a> Ibid</p>
</div>
<div id="ftn82">
<p><a href="#_ftnref82" name="_ftn82">[82]</a> http://venturebeat.com/2015/04/03/how-microsofts-using-big-data-to-predict-traffic-jams-up-to-an-hour-in-advance/</p>
</div>
<div id="ftn83">
<p><a href="#_ftnref83" name="_ftn83">[83]</a> Ibid</p>
</div>
<div id="ftn84">
<p><a href="#_ftnref84" name="_ftn84">[84]</a> https://www.hds.com/assets/pdf/the-hype-and-the-hope-summary.pdf</p>
</div>
<div id="ftn85">
<p><a href="#_ftnref85" name="_ftn85">[85]</a> <a href="http://www.news.cn/">http://www.news.cn</a> , 14/01/2015</p>
</div>
<div id="ftn86">
<p><a href="#_ftnref86" name="_ftn86">[86]</a> http://www.techgoondu.com/2015/06/29/plugging-the-big-data-skills-gap/</p>
</div>
<div id="ftn87">
<p><a href="#_ftnref87" name="_ftn87">[87]</a> Ibid</p>
</div>
<div id="ftn88">
<p><a href="#_ftnref88" name="_ftn88">[88]</a> Ibid</p>
</div>
<div id="ftn89">
<p><a href="#_ftnref89" name="_ftn89">[89]</a> http://www.zdnet.com/article/dell-to-create-big-data-skills-in-brazil/</p>
</div>
<div id="ftn90">
<p><a href="#_ftnref90" name="_ftn90">[90]</a> Ibid</p>
</div>
<div id="ftn91">
<p><a href="#_ftnref91" name="_ftn91">[91]</a> Efrati, Amir. "'Like' Button Follows Web Users." <i>The Wall Street Journal. </i>18 May 2011.</p>
<p><http://online.wsj.com/article/SB10001424052748704281504576329441432995616.html></p>
</div>
<div id="ftn92">
<p><a href="#_ftnref92" name="_ftn92">[92]</a> Krikpatrick, Robert. "Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience."</p>
<p><i>UN Global Pulse. </i> 16 Sept. 2011. <http://www.unglobalpulse.org/blog/data-philanthropy-public-privatesector-data-sharing-global-resilience></p>
</div>
<div id="ftn93">
<p><a href="#_ftnref93" name="_ftn93">[93]</a> Laney D (2001) 3D data management: Controlling data volume, velocity and variety. Available at: http://blogs. gartner.com/doug-laney/files/2012/01/ad949-3D-DataManagement-Controlling-Data-Volume-Velocity-andVariety.pdf</p>
</div>
<div id="ftn94">
<p><a href="#_ftnref94" name="_ftn94">[94]</a> Boyd D and Crawford K (2012) Critical questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication, & Society 15(5): 662-679.</p>
</div>
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For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis'>http://editors.cis-india.org/internet-governance/blog/big-data-in-the-global-south-an-analysis</a>
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No publishertanviInternet GovernanceBig Data2016-01-24T02:54:45ZBlog EntryBenefits and Harms of "Big Data"
http://editors.cis-india.org/internet-governance/blog/benefits-and-harms-of-big-data
<b>Today the quantity of data being generated is expanding at an exponential rate. From smartphones and televisions, trains and airplanes, sensor-equipped buildings and even the infrastructures of our cities, data now streams constantly from almost every sector and function of daily life.</b>
<h3 style="text-align: justify; "><b>Introduction</b></h3>
<p style="text-align: justify; ">In 2011 it was estimated that the quantity of data produced globally would surpass 1.8 zettabyte<a href="#_ftn1" name="_ftnref1">[1]</a>. By 2013 that had grown to 4 zettabytes<a href="#_ftn2" name="_ftnref2">[2]</a>, and with the nascent development of the so-called 'Internet of Things' gathering pace, these trends are likely to continue. This expansion in the volume, velocity, and variety of data available<a href="#_ftn3" name="_ftnref3">[3]</a> , together with the development of innovative forms of statistical analytics, is generally referred to as "Big Data"; though there is no single agreed upon definition of the term. Although still in its initial stages, Big Data promises to provide new insights and solutions across a wide range of sectors, many of which would have been unimaginable even 10 years ago.</p>
<p style="text-align: justify; ">Despite enormous optimism about the scope and variety of Big Data's potential applications however, many remain concerned about its widespread adoption, with some scholars suggesting it could generate as many harms as benefits<a href="#_ftn4" name="_ftnref4">[4]</a>. Most notably these have included concerns about the inevitable threats to privacy associated with the generation, collection and use of large quantities of data <a href="#_ftn5" name="_ftnref5">[5]</a>. However, concerns have also been raised regarding, for example, the lack of transparency around the design of algorithms used to process the data, over-reliance on Big Data analytics as opposed to traditional forms of analysis and the creation of new digital divides to just name a few.</p>
<p style="text-align: justify; ">The existing literature on Big Data is vast, however many of the benefits and harms identified by researchers tend to relate to sector specific applications of Big Data analytics, such as predictive policing, or targeted marketing. Whilst these examples can be useful in demonstrating the diversity of Big Data's possible applications, it can nevertheless be difficult to gain an overall perspective of the broader impacts of Big Data as a whole. As such this article will seek to disaggregate the potential benefits and harms of Big Data, organising them into several broad categories, which are reflective of the existing scholarly literature.</p>
<h3 style="text-align: justify; "><b>What are the potential benefits of Big Data?</b></h3>
<p style="text-align: justify; ">From politicians to business leaders, recent years have seen Big Data confidently proclaimed as a potential solution to a diverse range of problems from, world hunger and diseases, to government budget deficits and corruption. But if we look beyond the hyperbole and headlines, what do we really know about the advantages of Big Data? Given the current buzz surrounding it, the existing literature on Big Data is perhaps unsurprisingly vast, providing innumerable examples of the potential applications of Big Data from agriculture to policing. However, rather than try (and fail) to list the many possible applications of Big Data analytics across all sectors and industries, for the purposes of this article we have instead attempted to distil the various advantages of Big Data discussed within literature into the following five broad categories; Decision-Making, Efficiency & Productivity, Research & Development, Personalisation and Transparency, each of which will be discussed separately below.</p>
<p style="text-align: justify; "><i>Decision-Making </i></p>
<p style="text-align: justify; ">Whilst data analytics have always been used to improve the quality and efficiency of decision-making processes, the advent of Big Data means that the areas of our lives in which data driven decision- making plays a role is expanding dramatically; as businesses and governments become better able to exploit new data flows. Furthermore, the real-time and predictive nature of decision-making made possible by Big Data, are increasingly allowing these decisions to be automated. As a result, Big Data is providing governments and business with unprecedented opportunities to create new insights and solutions; becoming more responsive to new opportunities and better able to act quickly - and in some cases preemptively - to deal with emerging threats.</p>
<p style="text-align: justify; ">This ability of Big Data to speed up and improve decision-making processes can be applied across all sectors from transport to healthcare and is often cited within the literature as one of the key advantages of Big Data. Joh, for example, highlights the increased use of data driven predictive analysis by police forces to help them to forecast the times and geographical locations in which crimes are most likely to occur. This allows the force to redistribute their officers and resources according to anticipated need, and in certain cities has been highly effective in reducing crime rates <a href="#_ftn6" name="_ftnref6">[6]</a>. Raghupathi meanwhile cites the case of healthcare, where predictive modelling driven by big data is being used to proactively identify patients who could benefit from preventative care or lifestyle changes<a href="#_ftn7" name="_ftnref7">[7]</a>.</p>
<p style="text-align: justify; ">One area in particular where the decision-making capabilities of Big Data are having a significant impact is in the field of risk management <a href="#_ftn8" name="_ftnref8">[8]</a>. For instance, Big Data can allow companies to map their entire data landscape to help detect sensitive information, such as 16 digit numbers - potentially credit card data - which are not being stored according to regulatory requirements and intervene accordingly. Similarly, detailed analysis of data held about suppliers and customers can help companies to identify those in financial trouble, allowing them to act quickly to minimize their exposure to any potential default<a href="#_ftn9" name="_ftnref9">[9]</a>.</p>
<p style="text-align: justify; "><i>Efficiency and Productivity </i></p>
<p style="text-align: justify; ">In an era when many governments and businesses are facing enormous pressures on their budgets, the desire to reduce waste and inefficiency has never been greater. By providing the information and analysis needed for organisations to better manage and coordinate their operations, Big Data can help to alleviate such problems, leading to the better utilization of scarce resources and a more productive workforce <a href="#_ftn10" name="_ftnref10">[10]</a>.</p>
<p style="text-align: justify; ">Within the literature such efficiency savings are most commonly discussed in relation to reductions in energy consumption <a href="#_ftn11" name="_ftnref11">[11]</a>. For example, a report published by Cisco notes how the city of Olso has managed to reduce the energy consumption of street-lighting by 62 percent through the use of smart solutions driven by Big Data<a href="#_ftn12" name="_ftnref12">[12]</a>. Increasingly, however, statistical models generated by Big Data analytics are also being utilized to identify potential efficiencies in sourcing, scheduling and routing in a wide range of sectors from agriculture to transport. For example, Newell observes how many local governments are generating large databases of scanned license plates through the use of automated license plate recognition systems (ALPR), which government agencies can then use to help improve local traffic management and ease congestion<a href="#_ftn13" name="_ftnref13">[13]</a>.</p>
<p style="text-align: justify; ">Commonly these efficiency savings are only made possible by the often counter-intuitive insights generated by the Big Data models. For example, whilst a human analyst planning a truck route would always tend to avoid 'drive-bys' - bypassing one stop to reach a third before doubling back - Big Data insights can sometimes show such routes to be more efficient. In such cases efficiency saving of this kind would in all likelihood have gone unrecognised by a human analyst, not trained to look for such patterns<a href="#_ftn14" name="_ftnref14">[14]</a>.</p>
<p style="text-align: justify; "><i>Research, Development, and Innovation</i></p>
<p style="text-align: justify; ">Perhaps one of the most intriguing benefits of Big Data is its potential use in the research and development of new products and services. As is highlighted throughout the literature, Big Data can help businesses to gain an understanding of how others perceive their products or identify customer demand and adapt their marketing or indeed the design of their products accordingly<a href="#_ftn15" name="_ftnref15">[15]</a>. Analysis of social media data, for instance, can provide valuable insights into customers' sentiments towards existing products as well as discover demands for new products and services, allowing businesses to respond more quickly to changes in customer behaviour<a href="#_ftn16" name="_ftnref16">[16]</a>.</p>
<p style="text-align: justify; ">In addition to market research, Big Data can also be used during the design and development stage of new products; for example by helping to test thousands of different variations of computer-aided designs in an expedient and cost-effective manner. In doing so, business and designers are able to better assess how minor changes to a products design may affect its cost and performance, thereby improving the cost-effectiveness of the production process and increasing profitability.</p>
<p style="text-align: justify; "><i>Personalisation</i></p>
<p style="text-align: justify; ">For many consumers, perhaps the most familiar application of Big Data is its ability to help tailor products and services to meet their individual preferences. This phenomena is most immediately noticeable on many online services such as Netflix; where data about users activities and preferences is collated and analysed to provide a personalised service, for example by suggesting films or television shows the user may enjoy based upon their previous viewing history<a href="#_ftn17" name="_ftnref17">[17]</a>. By enabling companies to generate in-depth profiles of their customers, Big Data allows businesses to move past the 'one size fits all' approach to product and services design and instead quickly and cost-effectively adapt their services to better meet customer demand.</p>
<p style="text-align: justify; ">In addition to service personalisation, similar profiling techniques are increasingly being utilized in sectors such as healthcare. Here data about a patient's medical history, lifestyle, and even their gene expression patterns are collated, generating a detailed medical profile which can then be used to tailor treatments to meet their specific needs<a href="#_ftn18" name="_ftnref18">[18]</a>. Targeted care of this sort can not only help to reduce costs for example by helping to avoid over-prescriptions, but may also help to improve the effectiveness of treatments and so ultimately their outcome.</p>
<p style="text-align: justify; "><i>Transparency </i></p>
<p style="text-align: justify; ">If 'knowledge is power', then, - so say Big Data enthusiasts - advances in data analytics and the quantity of data available can give consumers and citizens the knowledge to hold governments and businesses to account, as well as make more informed choices about the products and services they use. Nevertheless, data (even lots of it) does not necessarily equal knowledge. In order for citizens and consumers to be able to fully utilize the vast quantities of data available to them, they must first have some way to make sense of it. For some, Big Data analytics provides just such a solution, allowing users to easily search, compare and analyze available data, thereby helping to challenge existing information asymmetries and make business and government more transparent<a href="#_ftn19" name="_ftnref19">[19]</a>.</p>
<p style="text-align: justify; ">In the private sector, Big Data enthusiasts have claimed that Big Data holds the potential to ensure complete transparency of supply chains, enabling concerned consumers to trace the source of their products, for example to ensure that they have been sourced ethically <a href="#_ftn20" name="_ftnref20">[20]</a>. Furthermore, Big Data is now making accessible information which was previously unavailable to average consumers and challenging companies whose business models rely on the maintenance of information asymmetries.The real-estate industry, for example, relies heavily upon its ability to acquire and control proprietary information, such as transaction data as a competitive asset. In recent years, however, many online services have allowed consumers to effectively bypass agents, by providing alternative sources of real-estate data and enabling prospective buyers and sellers to communicate directly with each other<a href="#_ftn21" name="_ftnref21">[21]</a>. Therefore, providing consumers with access to large quantities of actionable data . Big Data can help to eliminate established information asymmetries, allowing them to make better and more informed decisions about the products they buy and the services they enlist.</p>
<p style="text-align: justify; ">This potential to harness the power of Big Data to improve transparency and accountability can also be seen in the public sector, with many scholars suggesting that greater access to government data could help to stem corruption and make politics more accountable. This view was recently endorsed by the UN who highlighted the potential uses of Big Data to improve policymaking and accountability in a report published by the Independent Expert Advisory Group on the "Data Revolution for Sustainable Development". In the report experts emphasize the potential of what they term the 'data revolution', to help achieve sustainable development goals by for example helping civil society groups and individuals to 'develop data literacy and help communities and individuals to generate and use data, to ensure accountability and make better decisions for themselves' <a href="#_ftn22" name="_ftnref22">[22]</a>.</p>
<h3 style="text-align: justify; "><b>What are the potential harms of Big Data?</b></h3>
<p style="text-align: justify; ">Whilst it is often easy to be seduced by the utopian visions of Big Data evangelists, in order to ensure that Big Data can deliver the types of far-reaching benefits its proponents promise, it is vital that we are also sensitive to its potential harms. Within the existing literature, discussions about the potential harms of Big Data are perhaps understandably dominated by concerns about privacy. Yet as Big Data has begun to play an increasingly central role in our daily lives, a broad range of new threats have begun to emerge including issues related to security and scientific epistemology, as well as problems of marginalisation, discrimination and transparency; each of which will be discussed separately below.</p>
<h2 style="text-align: justify; ">Privacy</h2>
<p style="text-align: justify; ">By far the biggest concern raised by researchers in relation to Big Data is its risk to privacy. Given that by its very nature Big Data requires extensive and unprecedented access to large quantities of data; it is hardly surprising that many of the benefits outlined above in one way or another exist in tension with considerations of privacy. Although many scholars have called for a broader debate on the effects of Big Data on ethical best practice <a href="#_ftn23" name="_ftnref23"><sup><sup>[23]</sup></sup></a>, a comprehensive exploration into the complex debates surrounding the ethical implications of Big Data go far beyond the scope of this article. Instead we will simply attempt to highlight some of the major areas of concern expressed in the literature, including its effects on established principles of privacy and the implication of Big Data on the suitability of existing regulatory frameworks governing privacy and data protection.</p>
<p style="text-align: justify; ">1. Re-identification</p>
<p style="text-align: justify; ">Traditionally many Big Data enthusiasts have used de-identification - the process of anonymising data by removing personally identifiable information (PII) - as a way of justifying mass collection and use of personal data. By claiming that such measures are sufficient to ensure the privacy of users, data brokers, companies and governments have sought to deflect concerns about the privacy implications of Big Data, and suggest that it can be compliant with existing regulatory and legal frameworks on data protection.</p>
<p style="text-align: justify; ">However, many scholars remain concerned about the limits of anonymisation. As Tene and Polonetsky observe 'Once data-such as a clickstream or a cookie number-are linked to an identified individual, they become difficult to disentangle'<a href="#_ftn24" name="_ftnref24">[24]</a>. They cite the example of University of Texas researchers Narayanan and Shmatikov, who were able to successfully re-identify anonymised Netflix user data by cross referencing it with data stored in a publicly accessible online database. As Narayanan and Shmatikov themselves explained, 'once any piece of data has been linked to a person's real identity, any association between this data and a virtual identity breaks anonymity of the latter' <a href="#_ftn25" name="_ftnref25">[25]</a>. The quantity and variety of datasets which Big Data analytics has made associable with individuals is therefore expanding the scope of the types of data that can be considered PII, as well as undermining claims that de-identification alone is sufficient to ensure privacy for users.</p>
<p style="text-align: justify; ">2. Privacy Frameworks Obsolete?</p>
<p style="text-align: justify; ">In recent decades privacy and data protection frameworks based upon a number of so-called 'privacy principles' have formed the basis of most attempts to encourage greater consideration of privacy issues online<a href="#_ftn26" name="_ftnref26">[26]</a>. For many however, the emergence of Big Data has raised question about the extent to which these 'principles of privacy' are workable in an era of ubiquitous data collection.</p>
<p style="text-align: justify; "><i>Collection Limitation and Data Minimization</i> : Big Data by its very nature requires the collection and processing of very large and very diverse data sets. Unlike other forms scientific research and analysis which utilize various sampling techniques to identify and target the types of data most useful to the research questions, Big Data instead seeks to gather as much data as possible, in order to achieve full resolution of the phenomenon being studied, a task made much easier in recent years as a result of the proliferation of internet enabled devices and the growth of the Internet of Things. This goal of attaining comprehensive coverage exists in tension however with the key privacy principles of collection limitation and data minimization which seek to limit both the quantity and variety of data collected about an individual to the absolute minimum<a href="#_ftn27" name="_ftnref27">[27]</a>.</p>
<p style="text-align: justify; "><i>Purpose Limitation:</i> Since the utility of a given dataset is often not easily identifiable at the time of collection, datasets are increasingly being processed several times for a variety of different purposes. Such practices have significant implications for the principle of purpose limitation, which aims to ensure that organizations are open about their reasons for collecting data, and that they use and process the data for no other purpose than those initially specified <a href="#_ftn28" name="_ftnref28">[28]</a>.</p>
<p style="text-align: justify; "><i>Notice and Consent: </i> The principles of notice and consent have formed the cornerstones of attempts to protect privacy for decades. Nevertheless in an era of ubiquitous data collection, the notion that an individual must be required to provide their explicit consent to allow for the collection and processing of their data seems increasingly antiquated, a relic of an age when it was possible to keep track of your personal data relationships and transactions. Today as data streams become more complex, some have begun to question suitability of consent as a mechanism to protect privacy. In particular commentators have noted how given the complexity of data flows in the digital ecosystem most individuals are not well placed to make truly informed decisions about the management of their data<a href="#_ftn29" name="_ftnref29">[29]</a>. In one study, researchers demonstrated how by creating the perceptions of control, users were more likely to share their personal information, regardless of whether or not the users had actually gained control <a href="#_ftn30" name="_ftnref30">[30]</a>. As such, for many, the garnering of consent is increasingly becoming a symbolic box-ticking exercise which achieves little more than to irritate and inconvenience customers whilst providing a burden for companies and a hindrance to growth and innovation <a href="#_ftn31" name="_ftnref31">[31]</a>.</p>
<p style="text-align: justify; "><i>Access and Correction:</i> The principle of 'access and correction' refers to the rights of individuals to obtain personal information being held about them as well as the right to erase, rectify, complete or otherwise amend that data. Aside from the well documented problems with privacy self-management, for many the real-time nature of data generation and analysis in an era of Big Data poses a number of structural challenges to this principle of privacy. As x comments, 'a good amount of data is not pre-processed in a similar fashion as traditional data warehouses. This creates a number of potential compliance problems such as difficulty erasing, retrieving or correcting data. A typical big data system is not built for interactivity, but for batch processing. This also makes the application of changes on a (presumably) static data set difficult'<a href="#_ftn32" name="_ftnref32">[32]</a>.</p>
<p style="text-align: justify; "><i>Opt In-Out:</i> The notion that the provision of data should be a matter of personal choice on the part of the individual and that the individual can, if they chose decide to 'opt-out' of data collection, for example by ceasing use of a particular service, is an important component of privacy and data protection frameworks. The proliferation of internet-enabled devices, their integration into the built environment and the real-time nature of data collection and analysis however are beginning to undermine this concept. For many critics of Big Data the ubiquity of data collection points as well as the compulsory provision of data as a prerequisite for the access and use of many key online services is making opting-out of data collection not only impractical but in some cases impossible. <a href="#_ftn33" name="_ftnref33">[33]</a></p>
<p style="text-align: justify; ">3. "Chilling Effects"</p>
<p style="text-align: justify; ">For many scholars the normalization of large scale data collection is steadily producing a widespread perception of ubiquitous surveillance amongst users. Drawing upon Foucault's analysis of Jeremy Bentham's panopticon and the disciplinary effects of surveillance, they argue that this perception of permanent visibility can cause users to sub-consciously 'discipline' and self- regulate of their own behavior, fearful of being targeted or identified as 'abnormal' <a href="#_ftn34" name="_ftnref34">[34]</a>. As a result, the pervasive nature of Big Data risks generating a 'chilling effect' on user behavior and free speech.</p>
<p style="text-align: justify; ">Although the notion of "chilling effects" is quite prevalent throughout the academic literature on surveillance and security, the difficulty of quantifying the perception and effects of surveillance on online behavior and practices means that there have only been a limited number of empirical studies of this phenomena, and none directly related to the chilling effects of Big Data. One study, conducted by researchers at MIT however, sought to assess the impact of Edward Snowden's revelations about NSA surveillance programs on Google search trends. Nearly 6,000 participants were asked to individually rate certain keywords for their perceived degree of privacy sensitivity along multiple dimensions. Using Google's own publicly available search data, the researchers then analyzed search patterns for these terms before and after the Snowden revelations. In doing so they were able to demonstrate a reduction of around 2.2% in searchers for those terms deemed to be most sensitive in nature. According to the researchers themselves, the results 'suggest that there is a chilling effect on search behaviour from government surveillance on the Internet'<a href="#_ftn35" name="_ftnref35">[35]</a>. Although this study focussed on the effects on government surveillance, for many privacy advocates the growing pervasiveness of Big Data risks generating similar results. <a href="#_ftn36" name="_ftnref36">[36]</a></p>
<p style="text-align: justify; ">4. Dignitary Harms of Predictive Decision-Making</p>
<p style="text-align: justify; ">In addition to its potentially chilling effects on free speech, the automated nature of Big Data analytics also possess the potential to inflict so-called 'dignitary harms' on individuals, by revealing insights about themselves that they would have preferred to keep private <a href="#_ftn37" name="_ftnref37">[37]</a>.</p>
<p style="text-align: justify; ">In an infamous example, following a shopping trip to the retail chain Target, a young girl began to receive mail at her father's house advertising products for babies including, diapers, clothing, and cribs. In response, her father complained to the management of the company, incensed by what he perceived to be the company's attempts to "encourage" pregnancy in teens. A few days later however, the father was forced to contact the store again to apologies, after his daughter had confessed to him that she was indeed pregnant. It was later revealed that Target regularly analyzed the sale of key products such as supplements or unscented lotions in order to generate "pregnancy prediction" scores, which could be used to assess the likelihood that a customer was pregnant and to therefore target them with relevant offers<a href="#_ftn38" name="_ftnref38">[38]</a>. Such cases, though anecdotal illustrate how Big Data if not adopted sensitively can lead to potential embarrassing information about users being made public.</p>
<h2 style="text-align: justify; ">Security</h2>
<p style="text-align: justify; ">In relation to cybersecurity Big Data can be viewed to a certain extent as a double-edged sword. On the one hand, the unique capabilities of Big Data analytics can provide organizations with new and innovative methods of enhancing their cybersecurity systems. On the other however, the sheer quantity and diversity of data emanating from a variety of sources creates its own security risks.</p>
<p style="text-align: justify; ">5. "Honey-Pot"</p>
<p style="text-align: justify; ">The larger the quantities of confidential information stored by companies on their databases the more attractive those databases may appear to potential hackers.</p>
<p style="text-align: justify; ">6. Data Redundancy and Dispersion</p>
<p style="text-align: justify; ">Inherent to Big Data systems is the duplication of data to many locations in order to optimize query processing. Data is dispersed across a wide range of data repositories in different servers, in different parts of the world. As a result it may be difficult for organizations to accurately locate and secure all items of personal information.</p>
<h3 style="text-align: justify; "><b>Epistemological and Methodological Implications</b></h3>
<p style="text-align: justify; ">In 2008 Chris Anderson infamously proclaimed the 'end of theory'. Writing for Wired Magazine, Anderson predicted that the coming age of Big Data would create a 'deluge of data' so large that the scientific methods of hypothesis, sampling and testing would be rendered 'obsolete' <a href="#_ftn39" name="_ftnref39">[39]</a>. 'There is now a better way' Anderson insisted, 'Petabytes allow us to say: "Correlation is enough." We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot'<a href="#_ftn40" name="_ftnref40">[40]</a>.</p>
<p style="text-align: justify; ">In spite of these bold claims however, many theorists remain skeptical of Big Data's methodological benefits and have expressed concern about its potential implications for conventional scientific epistemologies. For them the increased prominence of Big Data analytics in science does not signal a paradigmatic transition to a more enlightened data-driven age, but a hollowing out of the scientific method and an abandonment of casual knowledge in favor of shallow correlative analysis<a href="#_ftn41" name="_ftnref41">[41]</a>.</p>
<p style="text-align: justify; "><i>7. </i> Obfuscation <i></i></p>
<p style="text-align: justify; ">Although Big Data analytics can be utilized to study almost any phenomena where enough data exists, many theorists have warned that simply because Big Data analytics <i>can</i> be used does not necessarily mean that they <i>should</i> be used<a href="#_ftn42" name="_ftnref42">[42]</a>. Bigger is not always better and indeed the sheer quantity of data made available to users may in fact act to obscure certain insights. Whereas traditional scientific methods use sampling techniques to identify the most important and relevant data, Big Data by contrast encourages the collection and use of as much data as possible, in an attempt to attain full resolution of the phenomena being studied. However, not all data is equally useful and simply inputting as much data as possible into an algorithm is unlikely to produce accurate results and may instead obscure key insights.</p>
<p style="text-align: justify; ">Indeed, whilst the promise of automation is central to a large part of Big Data's appeal, researchers observe that most Big Data analysis still requires an element of human judgement to filter out the 'good' data from the 'bad', and to decide what aspects of the data are relevant to the research objectives. As Boyd and Crawford observe, 'in the case of social media data, there is a 'data cleaning' process: making decisions about what attributes and variables will be counted, and which will be ignored. This process is inherently subjective"<a href="#_ftn43" name="_ftnref43"><sup><sup>[43]</sup></sup></a>.</p>
<p style="text-align: justify; ">Google's Flu Trend project provides an illustrative example of how Big Data's tendency to try to maximise data inputs can produce misleading results. Designed to accurately track flu outbreaks based upon data collected from Google searches, the project was initially proclaimed to be a great success. Gradually however it became apparent that the results being produced were not reflective of the reality on the ground. Later it was discovered that the algorithms used by the project to interpret search terms were insufficiently accurate to filter out anomalies in searches, such as those related to the 2009 H1N1 flu pandemic. As such, despite the great promise of Big Data, scholars insist it remains critical to be mindful of its limitations, remain selective about the types of data included in the analysis and exercise caution and intuition whenever interpreting its results <a href="#_ftn44" name="_ftnref44"><sup><sup>[44]</sup></sup></a>.</p>
<p style="text-align: justify; ">8. "Apophenia"</p>
<p style="text-align: justify; ">In complete contrast to the problem of obfuscation, Boyd and Crawford observe how Big Data may also lead to the practice of 'apophenia', a phenomena whereby analysts interpret patterns where none exist, 'simply because enormous quantities of data can offer connections that radiate in all directions" <a href="#_ftn45" name="_ftnref45"><sup><sup>[45]</sup></sup></a>. David Leinweber for example demonstrated that data mining techniques could show strong but ultimately spurious correlations between changes in the S&P 500 stock index and butter production in Bangladesh <a href="#_ftn46" name="_ftnref46">[46]</a>. Such spurious correlation between disparate and unconnected phenomena are a common feature of Big Data analytics and risks leading to unfounded conclusions being draw from the data.</p>
<p style="text-align: justify; ">Although Leinweber's primary focus of analysis was the use of Data-Mining technologies, his observations are equally applicable to Big Data. Indeed the tendency amongst Big Data analysts to marginalise the types of domain specific expertise capable of differentiating between relevant and irrelevant correlations in favour of algorithmic automation can in many ways be seen to exacerbate many of the problems Leinweber identified.</p>
<p style="text-align: justify; "><i>9. </i> From Causation to Correlation</p>
<p style="text-align: justify; ">Closely related to the problem of Aphonenia is the concern that Big Data's emphasis on correlative analysis risks leading to an abandonment of the pursuit of causal knowledge in favour of shallow descriptive accounts of scientific phenomena<a href="#_ftn47" name="_ftnref47">[47]</a>.</p>
<p style="text-align: justify; ">For many, Big Data enthusiasts 'correlation is enough', producing inherently meaningful results interpretable by anyone without the need for pre-existing theory or hypothesis. Whilst proponents of Big Data claim that such an approach allows them to produce objective knowledge, by cleansing the data of any kind of philosophical or ideological commitment, for others by neglecting the knowledge of domain experts, Big Data risks generating a shallow type of analysis, since it fails to adequately embed observations within a pre-existing body of knowledge.</p>
<p style="text-align: justify; ">This commitment to an empiricist epistemology and methodological monism is particularly problematic in the context of studies of human behaviour, where actions cannot be calculated and anticipated using quantifiable data alone. In such instances, a certain degree of qualitative analysis of social, historical and cultural variables may be required in order to make the data meaningful by embedding it within a broader body of knowledge. The abstract and intangible nature of these variables requires a great deal of expert knowledge and interpretive skill to comprehend. It is therefore vital that the knowledge of domain specific experts is properly utilized to help 'evaluate the inputs, guide the process, and evaluate the end products within the context of value and validity'<a href="#_ftn48" name="_ftnref48">[48]</a>.</p>
<p style="text-align: justify; ">As such, although Big Data can provide unrivalled accounts of "what" people do, it fundamentally fails to deliver robust explanations of "why" people do it. This problem is especially critical in the case of public policy-making since without any indication of the motivations of individuals, policy-makers can have no basis upon which to intervene to incentivise more positive outcomes.</p>
<h3 style="text-align: justify; "><b>Digital Divides and Marginalisation</b></h3>
<p style="text-align: justify; ">Today data is a highly valuable commodity. The market for data in and of itself has been steadily growing in recent years with the business models of many online services now formulated around the strategy of harvesting data from users<a href="#_ftn49" name="_ftnref49"><sup><sup>[49]</sup></sup></a>. As with the commodification of anything however, inequalities can easily emerge between the haves and have not's. Whilst the quantity of data currently generated on a daily basis is many times greater than at any other point in human history, the vast majority of this data is owned and tightly controlled by a very small number of technology companies and data brokers. Although in some instances limited access to data may be granted to university researchers or to those willing and able to pay a fee, in many cases data remains jealously guarded by data brokers, who view it as an important competitive asset. As a result these data brokers and companies risk becoming the gatekeepers of the Big Data revolution, adjudicating not only over who can benefit from Big Data, but also in what context and under what terms. For many such inconsistencies and inequalities in access to data raises serious doubts about just how widely distributed the benefits of Big Data will be. Others go even further claiming that far from helping to alleviate inequalities, the advent of Big Data risks exacerbating already significant digital divides that exist as well as creating new ones <a href="#_ftn50" name="_ftnref50"><sup><sup>[50]</sup></sup></a>.</p>
<p style="text-align: justify; ">10. Anti-Competitive Practices</p>
<p style="text-align: justify; ">As a result of the reluctance of large companies to share their data, there increasingly exists a divide in access between small start-ups companies and their larger and more established competitors. Thus, new entrants to the marketplace may be at a competitive disadvantage in relation to large and well established enterprises, being as they are unable to harness the analytical power of the vast quantities of data available to large companies by virtue of their privileged market position. Since the performance of many online services are today often intimately connected with the collation and use of users data, some researchers have suggested that this inequity in access to data could lead to a reduction in competition in the online marketplace, and ultimately therefore to less innovation and choice for consumers<a href="#_ftn51" name="_ftnref51">[51]</a>.</p>
<p style="text-align: justify; ">As a result researchers including Nathan Newman of New York University have called for a reassessment and reorientation of anti-trust investigations and regulatory approaches more generally to 'to focus on how control of personal data by corporations can entrench monopoly power and harm consumer welfare in an economy shaped increasingly by the power of "big data"'<a href="#_ftn52" name="_ftnref52">[52]</a>. Similarly a report produced by the European Data Protection Supervisor concluded that, 'The scope for abuse of market dominance and harm to the consumer through refusal of access to personal information and opaque or misleading privacy policies may justify a new concept of consumer harm for competition enforcement in digital economy' <a href="#_ftn53" name="_ftnref53">[53]</a>.</p>
<p style="text-align: justify; ">11. Research</p>
<p style="text-align: justify; ">From a research perspective barriers to access to data caused by proprietary control of datasets are problematic, since certain types of research could become restricted to those privileged enough to be granted access to data. Meanwhile those denied access are left not only incapable of conducting similar research projects, but also unable to test, verify or reproduce the findings of those who do. The existence of such gatekeepers may also lead to reluctance on the part of researchers to undertake research critical of the companies, upon whom they rely for access, leading to a chilling effect on the types of research conducted<a href="#_ftn54" name="_ftnref54">[54]</a>.</p>
<p style="text-align: justify; ">12. Inequality</p>
<p style="text-align: justify; ">Whilst bold claims are regularly made about the potential of Big Data to deliver economic development and generate new innovations, some critics of remain concerned about how equally the benefits of Big Data will be distributed and the effects this could have on already established digital divides <a href="#_ftn55" name="_ftnref55">[55]</a>.</p>
<p style="text-align: justify; ">Firstly, whilst the power of Big Data is already being utilized effectively by most economically developed nations, the same cannot necessarily be said for many developing countries. A combination of lower levels of connectivity, poor information infrastructure, underinvestment in information technologies and a lack of skills and trained personnel make it far more difficult for the developing world to fully reap the rewards of Big Data. As a consequence the Big Data revolution risks deepening global economic inequality as developing countries find themselves unable to compete with data rich nations whose governments can more easily exploit the vast quantities of information generated by their technically literate and connected citizens.</p>
<p style="text-align: justify; ">Likewise, to the extent that the Big Data analytics is playing a greater role in public policy-making, the capacity of individuals to generate large quantities of data, could potentially impact upon the extent to which they can provide inputs into the policy-making process. In a country such as India for example, where there exist high levels of inequality in access to information and communication technologies and the internet, there remain large discrepancies in the quantities of data produced by individuals. As a result there is a risk that those who lack access to the means of producing data will be disenfranchised, as policy-making processes become configured to accommodate the needs and interests of a privilege minority <a href="#_ftn56" name="_ftnref56">[56]</a>.</p>
<h3 style="text-align: justify; "><b>Discrimination</b></h3>
<p style="text-align: justify; ">13. Injudicious or Discriminatory Outcomes</p>
<p style="text-align: justify; ">Big Data presents the opportunity for governments, businesses and individuals to make better, more informed decisions at a much faster pace. Whilst this can evidently provide innumerable opportunities to increase efficiency and mitigate risk, by removing human intervention and oversight from the decision-making process Big Data analysts run the risk of becoming blind to unfair or injudicious results generated by skewed or discriminatory programming of the algorithms.</p>
<p style="text-align: justify; ">There currently exists a large number of automated decision-making algorithms in operation across a broad range of sectors including most notably perhaps those used to asses an individual's suitability for insurance or credit. In either of these cases faults in the programming or discriminatory assessment criteria can have potentially damaging implications for the individual, who may as a result be unable to attain credit or insurance. This concern with the potentially discriminatory aspects of Big Data is prevalent throughout the literature and real life examples have been identified by researchers in a large number of major sectors in which Big Data is currently being used<a href="#_ftn57" name="_ftnref57">[57]</a>.</p>
<p style="text-align: justify; ">Yu for instance, cites the case of the insurance company Progressive, which required its customers to install 'Snapsnot' - a small monitoring device - into their cars in order to receive their best rates. The device tracked and reported the customers driving habits, and offered discounts to those drivers who drove infrequently, broke smoothly, and avoided driving at night - behaviors that correlate with a lower risk of future accidents. Although this form of price differentiation provided incentives for customers to drive more carefully, it also had the unintended consequence of unfairly penalizing late-night shift workers. As Yu observes, 'for late night shift-workers, who are disproportionately poorer and from minority groups, this differential pricing provides no benefit at all. It categorizes them as similar to late-night party-goers, forcing them to carry more of the cost of the intoxicated and other irresponsible driving that happens disproportionately at night'<a href="#_ftn58" name="_ftnref58">[58]</a>.</p>
<p style="text-align: justify; ">In another example, it is noted how Big Data is increasingly being used to evaluate applicants for entry-level service jobs. One method of evaluating applicants is by the length of their commute - the rationale being that employees with shorter commutes are statistically more likely to remain in the job longer. However, since most service jobs are typically located in town centers and since poorer neighborhoods tend to be those on the outskirts of town, such criteria can have the effect of unfairly disadvantaging those living in economically deprived areas. Consequently such metrics of evaluation can therefore also unintentionally act to reinforce existing social inequalities by making it more difficult for economically disadvantaged communities to work their way out of poverty<a href="#_ftn59" name="_ftnref59">[59]</a>.</p>
<p style="text-align: justify; ">14. Lack of Algorithmic Transparency.</p>
<p style="text-align: justify; ">If data is indeed the 'oil of the 21<sup>st</sup> century'<a href="#_ftn60" name="_ftnref60">[60]</a> then algorithms are very much the engines which are driving innovation and economic development. For many companies the quality of their algorithms is often a crucial factor in providing them with a market advantage over their competitor. Given their importance, the secrets behind the programming of algorithms are often closely guarded by companies, and are typically classified as trade secrets and as such are protected by intellectual property rights. Whilst companies may claim that such secrecy is necessary to encourage market competition and innovation, many scholars are becoming increasingly concerned about the lack of transparency surrounding the design of these most crucial tools.</p>
<p style="text-align: justify; ">In particular there is a growing sentiment common amongst many researchers that there currently exists a chronic lack of accountability and transparency in terms of how Big Data algorithms are programmed and what criteria are used to determine outcomes <a href="#_ftn61" name="_ftnref61"><sup><sup>[61]</sup></sup></a>. As Frank Pasquale observed,</p>
<p style="text-align: justify; "><i>'</i> <i> hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy'<a href="#_ftn62" name="_ftnref62"><b>[62]</b></a>. </i></p>
<p style="text-align: justify; ">As such, without increased transparency in algorithmic design, instances of Big Data discrimination may go unnoticed as analyst are unable to access the information necessary to identify them.</p>
<h3 style="text-align: justify; "><b>Conclusion</b></h3>
<p style="text-align: justify; ">Today Big Data presents us with as many challenges as it does benefits. Whilst Big Data analytics can offer incredible opportunities to reduce inefficiency, improve decision-making, and increase transparency, concerns remain about the effects of these new technologies on issues such as privacy, equality and discrimination. Although the tensions between the competing demands of Big Data advocates and their critics may appear irreconcilable; only by highlighting these points of contestation can we hope to begin to ask the types of important and difficult questions necessary to do so, including; how can we reconcile Big Data's need for massive inputs of personal information with core principles of privacy such as data minimization and collection limitation? What processes and procedures need to be put in place during the design and implementation of Big Data models and algorithms to provide sufficient transparency and accountability so as to avoid instances of discrimination? What measures can be used to help close digital divides and ensure that the benefits of Big Data are shared equitably? Questions such as these are today only just beginning to be addressed; each however, will require careful consideration and reasoned debate, if Big Data is to deliver on its promises and truly fulfil its 'revolutionary' potential.</p>
<div style="text-align: justify; ">
<hr />
<div id="ftn1">
<p><a href="#_ftnref1" name="_ftn1">[1]</a> Gantz, J., &Reinsel, D. Extracting Value from Chaos, <i>IDC, </i>(2011), available at: <a href="http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf"> http://www.emc.com/collateral/analyst-reports/idc-extracting-value-from-chaos-ar.pdf </a></p>
</div>
<div id="ftn2">
<p><a href="#_ftnref2" name="_ftn2">[2]</a> Meeker, M. & Yu, L. Internet Trends, <i>Kleiner Perkins Caulfield Byers,</i> (2013), <a href="http://www.slideshare.net/kleinerperkins/kpcb-internet-trends-2013">http://www.slideshare.net/kleinerperkins/kpcb-internet-trends-2013</a> .</p>
</div>
<div id="ftn3">
<p><a href="#_ftnref3" name="_ftn3">[3]</a> Douglas, L<i>. </i> <a href="http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf"> <i>"3D Data Management: Controlling Data Volume, Velocity and Variety"</i> </a> <i> . Gartner, </i> (2001)</p>
</div>
<div id="ftn4">
<p><a href="#_ftnref4" name="_ftn4">[4]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a>, Tene, O., &Polonetsky, J. Big Data for All: Privacy and User Control in the Age of Analytics<i>, 11 Nw. J. Tech. &Intell. Prop. 239</i> (2013) <a href="http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1">http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1</a></p>
</div>
<div id="ftn5">
<p><a href="#_ftnref5" name="_ftn5">[5]</a> Ibid.,</p>
</div>
<div id="ftn6">
<p><a href="#_ftnref6" name="_ftn6">[6]</a> Joh. E, 'Policing by Numbers: Big Data and the Fourth Amendment', <i>Washington Law Review, Vol. 85: 35, </i>(2014) <a href="https://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1319/89WLR0035.pdf?sequence=1"> https://digital.law.washington.edu/dspace-law/bitstream/handle/1773.1/1319/89WLR0035.pdf?sequence=1 </a></p>
</div>
<div id="ftn7">
<p><a href="#_ftnref7" name="_ftn7">[7]</a> Raghupathi, W., &Raghupathi, V. <a href="http://www.hissjournal.com/content/2/1/3">Big data analytics in healthcare: promise and potential</a>. <i>Health Information Science and Systems</i>, (2014)</p>
</div>
<div id="ftn8">
<p><a href="#_ftnref8" name="_ftn8">[8]</a> Anderson, R., & Roberts, D. 'Big Data: Strategic Risks and Opportunities, <i>Crowe Horwarth Global Risk Consulting Limited</i>, (2012) <a href="https://www.crowehorwath.net/uploadedfiles/crowe-horwath-global/tabbed_content/big%20data%20strategic%20risks%20and%20opportunities%20white%20paper_risk13905.pdf"> https://www.crowehorwath.net/uploadedfiles/crowe-horwath-global/tabbed_content/big%20data%20strategic%20risks%20and%20opportunities%20white%20paper_risk13905.pdf </a></p>
</div>
<div id="ftn9">
<p><a href="#_ftnref9" name="_ftn9">[9]</a> Ibid.</p>
</div>
<div id="ftn10">
<p><a href="#_ftnref10" name="_ftn10">[10]</a> Kshetri. N, 'The Emerging role of Big Data in Key development issues: Opportunities, challenges, and concerns'. <i>Big Data & Society</i> (2014)<a href="http://bds.sagepub.com/content/1/2/2053951714564227.abstract">http://bds.sagepub.com/content/1/2/2053951714564227.abstract</a>,</p>
</div>
<div id="ftn11">
<p><a href="#_ftnref11" name="_ftn11">[11]</a> Tene, O., &Polonetsky, J. Big Data for All: Privacy and User Control in the Age of Analytics<i>, 11 Nw. J. Tech. &Intell. Prop. 239</i> (2013) <a href="http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1">http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1</a></p>
</div>
<div id="ftn12">
<p><a href="#_ftnref12" name="_ftn12">[12]</a> Cisco, 'IoE-Driven Smart Street Lighting Project Allows Oslo to Reduce Costs, Save Energy, Provide Better Service', Cisco, (2014) Available at: <a href="http://www.cisco.com/c/dam/m/en_us/ioe/public_sector/pdfs/jurisdictions/Oslo_Jurisdiction_Profile_051214REV.pdf"> http://www.cisco.com/c/dam/m/en_us/ioe/public_sector/pdfs/jurisdictions/Oslo_Jurisdiction_Profile_051214REV.pdf </a></p>
</div>
<div id="ftn13">
<p><a href="#_ftnref13" name="_ftn13">[13]</a> Newell, B, C. Local Law Enforcement Jumps on the Big Data Bandwagon: Automated License Plate Recognition Systems, Information Privacy, and Access to Government Information. <i>University of Washington - the Information School</i>, (2013) <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2341182">http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2341182</a></p>
</div>
<div id="ftn14">
<p><a href="#_ftnref14" name="_ftn14">[14]</a> Morris, D. Big data could improve supply chain efficiency-if companies would let it<i>, Fortune, August 5 </i>2015, http://fortune.com/2015/08/05/big-data-supply-chain/</p>
</div>
<div id="ftn15">
<p><a href="#_ftnref15" name="_ftn15">[15]</a> Tucker, Darren S., & Wellford, Hill B., Big Mistakes Regarding Big Data, Antitrust Source, American Bar Association, (2014). Available at SSRN: http://ssrn.com/abstract=2549044</p>
</div>
<div id="ftn16">
<p><a href="#_ftnref16" name="_ftn16">[16]</a> Davenport, T., Barth., Bean, R. How is Big Data Different, <i>MITSloan Management Review, Fall </i>(2012), Available at, <a href="http://sloanreview.mit.edu/article/how-big-data-is-different/">http://sloanreview.mit.edu/article/how-big-data-is-different/</a></p>
</div>
<div id="ftn17">
<p><a href="#_ftnref17" name="_ftn17">[17]</a> Tucker, Darren S., & Wellford, Hill B., Big Mistakes Regarding Big Data, Antitrust Source, American Bar Association, (2014). Available at SSRN: http://ssrn.com/abstract=2549044</p>
</div>
<div id="ftn18">
<p><a href="#_ftnref18" name="_ftn18">[18]</a> Raghupathi, W., &Raghupathi, V. <a href="http://www.hissjournal.com/content/2/1/3">Big data analytics in healthcare: promise and potential</a>. <i>Health Information Science and Systems</i>, (2014)</p>
</div>
<div id="ftn19">
<p><a href="#_ftnref19" name="_ftn19">[19]</a> Brown, B., Chui, M., Manyika, J. 'Are you Ready for the Era of Big Data?', <i>McKinsey Quarterly,</i> (2011), Available at, <a href="http://www.t-systems.com/solutions/download-mckinsey-quarterly-/1148544_1/blobBinary/Study-McKinsey-Big-data.pdf"> http://www.t-systems.com/solutions/download-mckinsey-quarterly-/1148544_1/blobBinary/Study-McKinsey-Big-data.pdf </a> ; Benady, D., 'Radical transparency will be unlocked by technology and big data', <i>Guardian </i>(2014) Available at: <a href="http://www.theguardian.com/sustainable-business/radical-transparency-unlocked-technology-big-data"> http://www.theguardian.com/sustainable-business/radical-transparency-unlocked-technology-big-data </a></p>
</div>
<div id="ftn20">
<p><a href="#_ftnref20" name="_ftn20">[20]</a> Ibid.</p>
</div>
<div id="ftn21">
<p><a href="#_ftnref21" name="_ftn21">[21]</a> Ibid.</p>
</div>
<div id="ftn22">
<p><a href="#_ftnref22" name="_ftn22">[22]</a> United Nations, A World That Counts: Mobilising the Data Revolution for Sustainable Development, <i> Report prepared at the request of the United Nations Secretary-General,by the Independent Expert Advisory Group on a Data Revolutionfor Sustainable Development. </i> (2014), pg. 18, see also, Hilbert, M. Big Data for Development: From Information- to Knowledge Societies (2013). Available at SSRN: <a href="http://ssrn.com/abstract=2205145">http://ssrn.com/abstract=2205145</a></p>
</div>
<div id="ftn23">
<p><a href="#_ftnref23" name="_ftn23">[23]</a> Greenleaf, G. Abandon All Hope? <i>Foreword for Issue 37(2) of the UNSW Law Journal on 'Communications Surveillance, Big Data, and the Law'</i> ,(2014) <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2490425">http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2490425##</a><span>, </span>Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i> Vol. 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a></p>
</div>
<div id="ftn24">
<p><a href="#_ftnref24" name="_ftn24">[24]</a> Tene, O., &Polonetsky, J. Big Data for All: Privacy and User Control in the Age of Analytics, 11 Nw. J. Tech. &Intell. Prop. 239 (2013) <a href="http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1">http://scholarlycommons.law.northwestern.edu/njtip/vol11/iss5/1</a></p>
</div>
<div id="ftn25">
<p><a href="#_ftnref25" name="_ftn25">[25]</a> Narayanan and Shmatikov quoted in Ibid.,</p>
</div>
<div id="ftn26">
<p><a href="#_ftnref26" name="_ftn26">[26]</a> OECD, Guidelines on the Protection of Privacy and Transborder Flows of Personal Data, The Organization for Economic Co-Operation and Development, (1999); The European Parliament and the Council of the European Union, EU Data Protection Directive, "Directive 95/46/EC of the European Parliament and of the Council of 24 October 1995 on the protection of individuals with regard to the processing of personal data and on the free movement of such data," (1995)</p>
</div>
<div id="ftn27">
<p><a href="#_ftnref27" name="_ftn27">[27]</a> Barocas, S., &Selbst, A, D., Big Data's Disparate Impact,<i>California Law Review, Vol. 104, </i>(2015). Available at SSRN: <a href="http://ssrn.com/abstract=2477899" target="_blank">http://ssrn.com/abstract=2477899</a></p>
</div>
<div id="ftn28">
<p><a href="#_ftnref28" name="_ftn28">[28]</a> Article 29 Working Group., Opinion 03/2013 on purpose limitation, <i>Article 29 Data Protection Working Party, </i>(2013) available at: <a href="http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2013/wp203_en.pdf"> http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommendation/files/2013/wp203_en.pdf </a></p>
</div>
<div id="ftn29">
<p><a href="#_ftnref29" name="_ftn29">[29]</a> Solove, D, J. Privacy Self-Management and the Consent Dilemma, 126 Harv. L. Rev. 1880 (2013), Available at: <a href="http://scholarship.law.gwu.edu/cgi/viewcontent.cgi?article=2093&context=faculty_publications"> http://scholarship.law.gwu.edu/cgi/viewcontent.cgi?article=2093&context=faculty_publications </a></p>
</div>
<div id="ftn30">
<p><a href="#_ftnref30" name="_ftn30">[30]</a> Brandimarte, L., Acquisti, A., & Loewenstein, G., Misplaced Confidences:</p>
<p>Privacy and the Control Paradox, <i>Ninth Annual Workshop on the Economics of Information Security (WEIS) June 7-8 2010, Harvard University, Cambridge, MA, </i>(2010), available at: <a href="https://fpf.org/wp-content/uploads/2010/07/Misplaced-Confidences-acquisti-FPF.pdf"> https://fpf.org/wp-content/uploads/2010/07/Misplaced-Confidences-acquisti-FPF.pdf </a></p>
</div>
<div id="ftn31">
<p><a href="#_ftnref31" name="_ftn31">[31]</a> Solove, D, J., Privacy Self-Management and the Consent Dilemma, <i>126 Harv. L. Rev. 1880</i> (2013), Available at: http://scholarship.law.gwu.edu/cgi/viewcontent.cgi?article=2093&context=faculty_publications</p>
</div>
<div id="ftn32">
<p><a href="#_ftnref32" name="_ftn32">[32]</a> Yu, W, E., Data., Privacy and Big Data-Compliance Issues and Considerations, <i>ISACA Journal, Vol. 3 2014 </i>(2014), available at: <a href="http://www.isaca.org/Journal/archives/2014/Volume-3/Pages/Data-Privacy-and-Big-Data-Compliance-Issues-and-Considerations.aspx"> http://www.isaca.org/Journal/archives/2014/Volume-3/Pages/Data-Privacy-and-Big-Data-Compliance-Issues-and-Considerations.aspx </a></p>
</div>
<div id="ftn33">
<p><a href="#_ftnref33" name="_ftn33">[33]</a> Ramirez, E., Brill, J., Ohlhausen, M., Wright, J., & McSweeny, T., Data Brokers: A Call for Transparency and Accountability, <i>Federal Trade Commission</i> (2014) https://www.ftc.gov/system/files/documents/reports/data-brokers-call-transparency-accountability-report-federal-trade-commission-may-2014/140527databrokerreport.pdf</p>
</div>
<div id="ftn34">
<p><a href="#_ftnref34" name="_ftn34">[34]</a> Michel Foucault, Discipline and Punish: The Birth of the Prison. Translated by Alan Sheridan, <i>London: Allen Lane, Penguin,</i> (1977)</p>
</div>
<div id="ftn35">
<p><a href="#_ftnref35" name="_ftn35">[35]</a> Marthews, A., & Tucker, C., Government Surveillance and Internet Search Behavior (2015), available at SSRN: http://ssrn.com/abstract=2412564</p>
</div>
<div id="ftn36">
<p><a href="#_ftnref36" name="_ftn36">[36]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon', Information, Communication & Society, Vol. 15, Issue 5, (2012)</p>
</div>
<div id="ftn37">
<p><a href="#_ftnref37" name="_ftn37">[37]</a> Hirsch, D., That's Unfair! Or is it? Big Data, Discrimination and the FTC's Unfairness Authority, <i>Kentucky Law Journal, Vol. 103</i>, available at: <a href="http://www.kentuckylawjournal.org/wp-content/uploads/2015/02/103KyLJ345.pdf"> http://www.kentuckylawjournal.org/wp-content/uploads/2015/02/103KyLJ345.pdf </a></p>
</div>
<div id="ftn38">
<p><a href="#_ftnref38" name="_ftn38">[38]</a> Hill, K., How Target Figured Out A Teen Girl Was Pregnant Before Her Father Didhttp://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/</p>
</div>
<div id="ftn39">
<p><a href="#_ftnref39" name="_ftn39">[39]</a> Anderson, C (2008) "The End of Theory: The Data Deluge Makes the Scientific Method Obsolete", WIRED, June 23 2008, www.wired.com/2008/06/pb-theory/</p>
</div>
<div id="ftn40">
<p><a href="#_ftnref40" name="_ftn40">[40]</a> Ibid.,</p>
</div>
<div id="ftn41">
<p><a href="#_ftnref41" name="_ftn41">[41]</a> Kitchen, R (2014) Big Data, new epistemologies and paradigm shifts, Big Data & Society, April-June 2014: 1-12</p>
</div>
<div id="ftn42">
<p><a href="#_ftnref42" name="_ftn42">[42]</a> Boyd D and Crawford K (2012) Critical questions for big data. Information, Communication and Society 15(5): 662-679</p>
</div>
<div id="ftn43">
<p><a href="#_ftnref43" name="_ftn43">[43]</a> Ibid</p>
</div>
<div id="ftn44">
<p><a href="#_ftnref44" name="_ftn44">[44]</a> Lazer, D., Kennedy, R., King, G., &Vespignani, A. " <a href="http://gking.harvard.edu/publications/parable-Google-Flu%c2%a0Traps-Big-Data-Analysis"> The Parable of Google Flu: Traps in Big Data Analysis </a> ." <i>Science 343</i> (2014): 1203-1205. Copy at <a href="http://j.mp/1ii4ETo">http://j.mp/1ii4ETo</a></p>
</div>
<div id="ftn45">
<p><a href="#_ftnref45" name="_ftn45">[45]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a></p>
</div>
<div id="ftn46">
<p><a href="#_ftnref46" name="_ftn46">[46]</a> Leinweber, D. (2007) 'Stupid data miner tricks: overfitting the S&P 500', The Journal of Investing, vol. 16, no. 1, pp. 15-22. <a href="http://m.shookrun.com/documents/stupidmining.pdf">http://m.shookrun.com/documents/stupidmining.pdf</a></p>
</div>
<div id="ftn47">
<p><a href="#_ftnref47" name="_ftn47">[47]</a> Boyd D and Crawford K (2012) Critical questions for big data. Information, Communication and Society 15(5): 662-679</p>
</div>
<div id="ftn48">
<p><a href="#_ftnref48" name="_ftn48">[48]</a> McCue, C., Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis, <i>Butterworth-Heinemann,</i> (2014)</p>
</div>
<div id="ftn49">
<p><a href="#_ftnref49" name="_ftn49">[49]</a> De Zwart, M. J., Humphreys, S., & Van Dissel, B. Surveillance, big data and democracy: lessons for Australia from the US and UK. <i>Http://www.unswlawjournal.unsw.edu.au/issue/volume-37-No-2</i>. (2014) Retrieved from https://digital.library.adelaide.edu.au/dspace/handle/2440/90048</p>
</div>
<div id="ftn50">
<p><a href="#_ftnref50" name="_ftn50">[50]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a>; Newman, N., Search, Antitrust and the Economics of the Control of User Data, <i>31 YALE J. ON REG. 401 </i>(2014)</p>
</div>
<div id="ftn51">
<p><a href="#_ftnref51" name="_ftn51">[51]</a> Newman, N., The Cost of Lost Privacy: Search, Antitrust and the Economics of the Control of User Data (2013). Available at SSRN: http://ssrn.com/abstract=2265026, Newman, N. ,Search, Antitrust and the Economics of the Control of User Data, <i>31 YALE J. ON REG. 401</i> (2014)</p>
</div>
<div id="ftn52">
<p><a href="#_ftnref52" name="_ftn52">[52]</a> Ibid.,</p>
</div>
<div id="ftn53">
<p><a href="#_ftnref53" name="_ftn53">[53]</a> European Data Protection Supervisor, Privacy and competitiveness in the age of big data:</p>
<p>The interplay between data protection, competition law and consumer protection in the Digital Economy, (2014), available at: <a href="https://secure.edps.europa.eu/EDPSWEB/webdav/shared/Documents/Consultation/Opinions/2014/14-03-26_competitition_law_big_data_EN.pdf"> https://secure.edps.europa.eu/EDPSWEB/webdav/shared/Documents/Consultation/Opinions/2014/14-03-26_competitition_law_big_data_EN.pdf </a></p>
</div>
<div id="ftn54">
<p><a href="#_ftnref54" name="_ftn54">[54]</a> Boyd, D., and Crawford, K. 'Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon'<i>, </i><i>Information, Communication & Society,</i>Vol 15, Issue 5, (2012) <a href="http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878">http://www.tandfonline.com/doi/abs/10.1080/1369118X.2012.678878</a></p>
</div>
<div id="ftn55">
<p><a href="#_ftnref55" name="_ftn55">[55]</a> Schradie, J., Big Data Not Big Enough? How the Digital Divide Leaves People Out, MediaShift, 31 July 2013, (2013), available at: <a href="http://mediashift.org/2013/07/big-data-not-big-enough-how-digital-divide-leaves-people-out/"> http://mediashift.org/2013/07/big-data-not-big-enough-how-digital-divide-leaves-people-out/ </a></p>
</div>
<div id="ftn56">
<p><a href="#_ftnref56" name="_ftn56">[56]</a> Crawford, K., The Hidden Biases in Big Data, <i>Harvard Business Review, 1 April 2013 </i>(2013), available at: <a href="https://hbr.org/2013/04/the-hidden-biases-in-big-data">https://hbr.org/2013/04/the-hidden-biases-in-big-data</a></p>
</div>
<div id="ftn57">
<p><a href="#_ftnref57" name="_ftn57">[57]</a> Robinson, D., Yu, H., Civil Rights, Big Data, and Our Algorithmic Future, (2014) <a href="http://bigdata.fairness.io/introduction/">http://bigdata.fairness.io/introduction/</a></p>
</div>
<div id="ftn58">
<p><a href="#_ftnref58" name="_ftn58">[58]</a> Ibid.</p>
</div>
<div id="ftn59">
<p><a href="#_ftnref59" name="_ftn59">[59]</a> Ibid</p>
</div>
<div id="ftn60">
<p><a href="#_ftnref60" name="_ftn60">[60]</a> Rotellla, P., Is Data The New Oil? Forbes, 2 April 2012, (2012), available at: <a href="http://www.forbes.com/sites/perryrotella/2012/04/02/is-data-the-new-oil/"> http://www.forbes.com/sites/perryrotella/2012/04/02/is-data-the-new-oil/ </a></p>
</div>
<div id="ftn61">
<p><a href="#_ftnref61" name="_ftn61">[61]</a> Barocas, S., &Selbst, A, D., Big Data's Disparate Impact,<i>California Law Review, Vol. 104, </i>(2015). Available at SSRN: <a href="http://ssrn.com/abstract=2477899" target="_blank">http://ssrn.com/abstract=2477899</a>; Kshetri. N, 'The Emerging role of Big Data in Key development issues: Opportunities, challenges, and concerns'. <i>Big Data & Society</i>(2014) <a href="http://bds.sagepub.com/content/1/2/2053951714564227.abstract">http://bds.sagepub.com/content/1/2/2053951714564227.abstract</a></p>
</div>
<div id="ftn62">
<p><a href="#_ftnref62" name="_ftn62">[62]</a> Pasquale, F., The Black Box Society: The Secret Algorithms That Control Money and Information, Harvard University Press , (2015)</p>
</div>
</div>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/benefits-and-harms-of-big-data'>http://editors.cis-india.org/internet-governance/blog/benefits-and-harms-of-big-data</a>
</p>
No publisherScott MasonInternet GovernanceBig Data2015-12-30T02:48:08ZBlog EntryPredictive Policing: What is it, How it works, and its Legal Implications
http://editors.cis-india.org/internet-governance/blog/predictive-policing-what-is-it-how-it-works-and-it-legal-implications
<b>This article reviews literature surrounding big data and predictive policing and provides an analysis of the legal implications of using predictive policing techniques in the Indian context.</b>
<h2 style="text-align: justify; ">Introduction</h2>
<p style="text-align: justify; ">For the longest time, humans have been obsessed with prediction. Perhaps the most well-known oracle in history, Pythia, the infallible Oracle of Delphi was said to predict future events in hysterical outbursts on the seventh day of the month, inspired by the god Apollo himself. This fascination with informing ourselves about future events has hardly subsided in us humans. What has changed however is the methods we employ to do so. The development of Big data technologies for one, has seen radical applications into many parts of life as we know it, including enhancing our ability to make accurate predictions about the future.</p>
<p style="text-align: justify; ">One notable application of Big data into prediction caters to another basic need since the dawn of human civilisation, the need to protect our communities and cities. The word 'police' itself originates from the Greek word '<i>polis'</i>, which means city. The melding of these two concepts prediction and policing has come together in the practice of Predictive policing, which is the application of computer modelling to historical crime data and metadata to predict future criminal activity<a href="#_ftn1" name="_ftnref1">[1]</a><b>. </b>In the subsequent sections, I will attempt an introduction of predictive policing and explain some of the main methods within the domain of predictive policing. Because of the disruptive nature of these technologies, it will also be prudent to expand on the implications predictive technologies have for justice, privacy protections and protections against discrimination among others.</p>
<p style="text-align: justify; ">In introducing the concept of predictive policing, my first step is to give a short explanation about current predictive analytics techniques, because these techniques are the ones which are applied into a law enforcement context as predictive policing.</p>
<h2 style="text-align: justify; ">What is predictive analysis</h2>
<p style="text-align: justify; ">Facilitated by the availability of big data, predictive analytics uses algorithms to recognise data patterns and predict future outcomes<a href="#_ftn2" name="_ftnref2">[2]</a>. Predictive analytics encompasses data mining, predictive modeling, machine learning, and forecasting<a href="#_ftn3" name="_ftnref3">[3]</a>. Predictive analytics also relies heavily on machine learning and artificial intelligence approaches <a href="#_ftn4" name="_ftnref4">[4]</a>. The aim of such analysis is to identify relationships among variables that may not be immediately apparent using hypothesis-driven methods.<a href="#_ftn5" name="_ftnref5">[5]</a> In the mainstream media, one of the most infamous stories about the use of predictive analysis comes from USA, regarding a department store Target and their data analytics practices <a href="#_ftn6" name="_ftnref6">[6]</a>. Target mined data from purchasing patterns of people who signed onto their baby registry. From this they were able to predict approximately when customers may be due and target advertisements accordingly. In the noted story, they were so successful that they predicted pregnancy before the pregnant girl's father knew she was pregnant. <a href="#_ftn7" name="_ftnref7">[7]</a></p>
<h3 style="text-align: justify; ">Examples of predictive analytics</h3>
<ul>
<li>Predicting the success of a movie based on its online ratings<a href="#_ftn8" name="_ftnref8">[8]</a></li>
<li>Many universities, sometimes in partnership with other firms use predictive analytics to provide course recommendations to students, track student performance, personalize curriculum to individual students and foster networking between students.<a href="#_ftn9" name="_ftnref9">[9]</a></li>
<li>Predictive Analysis of Corporate Bond Indices Returns<a href="#_ftn10" name="_ftnref10">[10]</a></li>
</ul>
<h2 style="text-align: justify; ">Relationship between predictive analytics and predictive policing</h2>
<p style="text-align: justify; ">The same techniques used in many of the predictive methods mentioned above find application into some predictive policing methods. However two important points need to be raised:</p>
<p style="text-align: justify; ">First, predictive analytics is actually a subset of predictive policing. This is because while the steps in creating a predictive model, of defining a target variable, exposing your model to training data, selecting appropriate features and finally running predictive analysis <a href="#_ftn11" name="_ftnref11">[11]</a> maybe the same in a policing context, there are other methods which may be used to predict crime, but which do not rely on data mining. These techniques may instead use other methods, such as some of those detailed below along with data about historical crime to generate predictions.</p>
<p style="text-align: justify; ">In her article "Policing by Numbers: Big Data and the Fourth Amendment"<a href="#_ftn12" name="_ftnref12">[12]</a>, Joh categorises 3 main applications of Big data into policing. These are Predictive Policing, Domain Awareness systems and Genetic Data Banks. Genetic data banks refer to maintaining large databases of DNA that was collected as part of the justice system. Issues arise when the DNA collected is repurposed in order to conduct familial searches, instead of being used for corroborating identity. Familial searches may have disproportionate impacts on minority races. Domain Awareness systems use various computer software and other digital surveillance tools such as Geographical Information Systems <a href="#_ftn13" name="_ftnref13">[13]</a> or more illicit ones such as Black Rooms<a href="#_ftn14" name="_ftnref14">[14]</a> to "help police create a software-enhanced picture of the present, using thousands of data points from multiple sources within a city" <a href="#_ftn15" name="_ftnref15">[15]</a>. I believe Joh was very accurate in separating Predictive Policing from Domain Awareness systems, especially when it comes to analysing the implications of the various applications of Big data into policing.</p>
<p style="text-align: justify; ">In such an analysis of the implications of using predictive policing methods, the issues surrounding predictive technologies often get conflated with larger issues about the application of big data into law enforcement. That opens the debate up to questions about overly intrusive evidence gathering and mass surveillance systems, which though used along with predictive technology, are not themselves predictive in nature. In this article, I aim to concentrate on the specific implications that arise due to predictive methods.</p>
<p style="text-align: justify; ">One important point regarding the impact of predictive policing is how the insights that predictive policing methods offer are used. There is much support for the idea that predictive policing does not replace policing methods, but actually augments them. The RAND report specifically cites one myth about predictive policing as "the computer will do everything for you<a href="#_ftn16" name="_ftnref16">[16]</a>". In reality police officers need to act on the recommendations provided by the technologies.</p>
<h2 style="text-align: justify; ">What is Predictive policing?</h2>
<p style="text-align: justify; ">Predictive policing is the "application of analytical techniques-particularly quantitative techniques-to identify likely targets for police intervention and prevent crime or solve past crimes by making statistical predictions".<a href="#_ftn17" name="_ftnref17">[17]</a> It is important to note that the use of data and statistics to inform policing is not new. Indeed, even twenty years ago, before the deluge of big data we have today, law enforcement regimes such as the New York Police Department (NYPD) were already using crime data in a major way. In order to keep track of crime trends, NYPD used the software CompStat<a href="#_ftn18" name="_ftnref18">[18]</a> to map "crime statistics along with other indicators of problems, such as the locations of crime victims and gun arrests"<a href="#_ftn19" name="_ftnref19">[19]</a>. The senior officers used the information provided by CompStat to monitor trends of crimes on a daily basis and such monitoring became an instrumental way to track the performance of police agencies<a href="#_ftn20" name="_ftnref20">[20]</a>. CompStat has since seen application in many other jurisdictions <a href="#_ftn21" name="_ftnref21">[21]</a>.</p>
<p style="text-align: justify; ">But what is new is the amount of data available for collection, as well as the ease with which organisations can analyse and draw insightful results from that data. Specifically, new technologies allow for far more rigorous interrogation of data and wide-ranging applications, including adding greater accuracy to the prediction of future incidence of crime.</p>
<h2 style="text-align: justify; ">Predictive Policing methods</h2>
<p style="text-align: justify; ">Some methods of predictive policing involve application of known standard statistical methods, while other methods involve modifying these standard techniques. Predictive techniques that forecast future criminal activities can be framed around six analytic categories. They all may overlap in the sense that multiple techniques are used to create actual predictive policing software and in fact it is similar theories of criminology which undergird many of these methods, but the categorisation in such a way helps clarify the concept of predictive policing. The basis for the categorisation below comes from a RAND Corporation report entitled 'Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations' <a href="#_ftn22" name="_ftnref22">[22]</a>, which is a comprehensive and detailed contribution to scholarship in this nascent area.</p>
<p style="text-align: justify; ">Hot spot analysis: Methods involving hot spot analysis attempt to "predict areas of increased crime risk based on historical crime data"<a href="#_ftn23" name="_ftnref23">[23]</a>. The premise behind such methods lies in the adage that "crime tends to be lumpy" <a href="#_ftn24" name="_ftnref24">[24]</a>. Hot Spot analysis seeks to map out these previous incidences of crime in order to inform potential future crime.</p>
<p style="text-align: justify; ">Regression methods: A regression aims to find relationships between independent variables (factors that may influence criminal activity) and certain variables that one aims to predict. Hence, this method would track more variables than just crime history.</p>
<p style="text-align: justify; ">Data mining techniques: Data mining attempts to recognise patterns in data and use it to make predictions about the future. One important variant in the various types of data mining methods used in policing are different types of algorithms that are used to mine data in different ways. These are dependent on the nature of the data the predictive model was trained on and will be used to interrogate in the future. Two broad categories of algorithms commonly used are clustering algorithms and classification algorithms:</p>
<p style="text-align: justify; ">· Clustering algorithms "form a class of data mining approaches that seek to group data into clusters with similar attributes" <a href="#_ftn25" name="_ftnref25">[25]</a>. One example of clustering algorithms is spatial clustering algorithms, which use geospatial crime incident data to predict future hot spots for crime<a href="#_ftn26" name="_ftnref26">[26]</a>.</p>
<p style="text-align: justify; ">· Classification algorithms "seek to establish rules assigning a class or label to events"<a href="#_ftn27" name="_ftnref27">[27]</a>. These algorithms use training data sets "to learn the patterns that determine the class of an observation"<a href="#_ftn28" name="_ftnref28">[28]</a> The patterns identified by the algorithm will be applied to future data, and where applicable, the algorithm will recognise similar patterns in the data. This can be used to make predictions about future criminal activity for example.</p>
<p style="text-align: justify; ">Near-repeat methods: Near-repeat methods work off the assumption that future crimes will take place close to timing and location of current crimes. Hence, it could be postulated that areas of high crime will experience more crime in the near future<a href="#_ftn29" name="_ftnref29">[29]</a>. This involves the use of a 'self-exciting' algorithm, very similar to algorithms modelling earthquake aftershocks <a href="#_ftn30" name="_ftnref30">[30]</a>. The premise undergirding such methods is very similar to that of hot spot analysis.</p>
<p style="text-align: justify; ">Spatiotemporal analysis<b>: </b>Using "environmental and temporal features of the crime location" <a href="#_ftn31" name="_ftnref31">[31]</a> as the basis for predicting future crime. By combining the spatiotemporal features of the crime area with crime incident data, police could use the resultant information to predict the location and time of future crimes. Examples of factors that may be considered include timing of crimes, weather, distance from highways, time from payday and many more.</p>
<p style="text-align: justify; ">Risk terrain analysis: Analyses other factors that are useful in predicting crimes. Examples of such factors include "the social, physical, and behavioural factors that make certain areas more likely to be affected by crime"<a href="#_ftn32" name="_ftnref32">[32]</a></p>
<p style="text-align: justify; ">Various methods listed above are used, often together, to predict the where and when a crime may take place or even potential victims. The unifying thread which relates these methods is their dependence on historical crime data.</p>
<h2 style="text-align: justify; ">Examples of predictive policing:</h2>
<p style="text-align: justify; ">Most uses of predictive policing that have been studied and reviewed in scholarly work come from the USA, though I will detail one case study from Derbyshire, UK. Below is a collation of various methods that are a practical application of the methods raised above.</p>
<p style="text-align: justify; ">Hot Spot analysis in Sacramento: In February 2011, Sacramento Police Department began using hot spot analysis along with research on optimal patrol time to act as a sufficient deterrent to inform how they patrol high-risk areas. This policy was aimed at preventing serious crimes by patrolling these predicted hot spots. In places where there was such patrolling, serious crimes reduced by a quarter with no significant increases such crimes in surrounding areas<a href="#_ftn33" name="_ftnref33">[33]</a>.</p>
<p style="text-align: justify; ">Data Mining and Hot Spot Mapping in Derbyshire, UK: The Safer Derbyshire Partnership, a group of law enforcement agencies and municipal authorities sought to identify juvenile crime hotspots<a href="#_ftn34" name="_ftnref34">[34]</a>. They used MapInfo software to combine "multiple discrete data sets to create detailed maps and visualisations of criminal activity, including temporal and spatial hotspots" <a href="#_ftn35" name="_ftnref35">[35]</a>. This information informed law enforcement about how to optimally deploy their resources.</p>
<p style="text-align: justify; ">Regression models in Pittsburgh: Researchers used reports from Pittsburgh Bureau of Police about violent crimes and "leading indicator" <a href="#_ftn36" name="_ftnref36">[36]</a> crimes, crimes that were relatively minor but which could be a sign of potential future violent offences. The researcher ran analysis of areas with violent crimes, which were used as the dependent variable in analysing whether violent crimes in certain areas could be predicted by the leading indicator data. From the 93 significant violent crime areas that were studied, 19 areas were successfully predicted by the leading indicator data.<a href="#_ftn37" name="_ftnref37">[37]</a></p>
<p style="text-align: justify; ">Risk terrain modelling analysis in Morris County, New Jersey: Police in Morris County, used risk terrain analysis to tackle violent crimes and burglaries. They considered five inputs in their model: "past burglaries, the address of individuals recently arrested for property crimes, proximity to major highways, the geographic concentration of young men and the location of apartment complexes and hotels." <a href="#_ftn38" name="_ftnref38">[38]</a> The Morris County law enforcement officials linked the significant reductions in violent and property crime to their use of risk terrain modelling<a href="#_ftn39" name="_ftnref39">[39]</a>.</p>
<p style="text-align: justify; ">Near-repeat & hot spot analysis used by Santa Cruz Police Department: Uses PredPol software that applies the Mohler's algorithm <a href="#_ftn40" name="_ftnref40">[40]</a> to a database with five years' worth of crime data to assess the likelihood of future crime occurring in the geographic areas within the city. Before going on shift, officers receive information identifying 15 such areas with the highest probability of crime<a href="#_ftn41" name="_ftnref41">[41]</a>. The initiative has been cited as being very successful at reducing burglaries, and was used in Los Angeles and Richmond, Virginia<a href="#_ftn42" name="_ftnref42">[42]</a>.</p>
<p style="text-align: justify; ">Data Mining and Spatiotemporal analysis to predict future criminal activities in Chicago: Officers in Chicago Police Department made visits to people their software predicted were likely to be involved in violent crimes<a href="#_ftn43" name="_ftnref43">[43]</a>, guided by an algorithm-generated "Heat List"<a href="#_ftn44" name="_ftnref44">[44]</a>. Some of the inputs used in the predictions include some types of arrest records, gun ownership, social networks<a href="#_ftn45" name="_ftnref45">[45]</a> (police analysis of social networking is also a rising trend in predictive policing<a href="#_ftn46" name="_ftnref46">[46]</a>) and generally type of people you are acquainted with <a href="#_ftn47" name="_ftnref47">[47]</a> among others, but the full list of the factors are not public. The list sends police officers (or sometimes mails letters) to peoples' homes to offer social services or deliver warnings about the consequences for offending. Based in part on the information provided by the algorithm, officers may provide people on the Heat List information about vocational training programs or warnings about how Federal Law provides harsher punishments for reoffending<a href="#_ftn48" name="_ftnref48">[48]</a>.</p>
<h2 style="text-align: justify; ">Predictive policing in India</h2>
<p style="text-align: justify; ">In this section, I map out some of the developments in the field of predictive policing within India. On the whole, predictive policing is still very new in India, with Jharkhand being the only state that appears to already have concrete plans in place to introduce predictive policing.</p>
<h3 style="text-align: justify; ">Jharkhand Police</h3>
<p style="text-align: justify; ">The Jharkhand police began developing their IT infrastructure such as a Geographic Information System (GIS) and Server room when they received funding for Rs. 18.5 crore from the Ministry of Home Affairs<a href="#_ftn49" name="_ftnref49">[49]</a>. The Open Group on E-governance (OGE), founded as a collaboration between the Jharkhand Police and National Informatics Centre<a href="#_ftn50" name="_ftnref50">[50]</a>, is now a multi-disciplinary group which takes on different projects related to IT<a href="#_ftn51" name="_ftnref51">[51]</a>. With regards to predictive policing, some members of OGE began development in 2013 of data mining software which will scan online records that are digitised. The emerging crime trends "can be a building block in the predictive policing project that the state police want to try."<a href="#_ftn52" name="_ftnref52">[52]</a></p>
<p style="text-align: justify; ">The Jharkhand Police was also reported in 2012 to be in the final stages of forming a partnership with IIM-Ranchi<a href="#_ftn53" name="_ftnref53">[53]</a>. It was alleged the Jharkhand police aimed to tap into IIM's advanced business analytics skills <a href="#_ftn54" name="_ftnref54">[54]</a>, skills that can be very useful in a predictive policing context. Mr Pradhan suggested that "predictive policing was based on intelligence-based patrol and rapid response"<a href="#_ftn55" name="_ftnref55">[55]</a> and that it could go a long way to dealing with the threat of Naxalism in Jharkhand<a href="#_ftn56" name="_ftnref56">[56]</a>.</p>
<p style="text-align: justify; ">However, in Jharkhand, the emphasis appears to be targeted at developing a massive Domain Awareness system, collecting data and creating new ways to present that data to officers on the ground, instead of architecting and using predictive policing software. For example, the Jharkhand police now have in place "a Naxal Information System, Crime Criminal Information System (to be integrated with the CCTNS) and a GIS that supplies customised maps that are vital to operations against Maoist groups"<a href="#_ftn57" name="_ftnref57">[57]</a>. The Jharkhand police's "Crime Analytics Dashboard" <a href="#_ftn58" name="_ftnref58">[58]</a> shows the incidence of crime according to type, location and presents it in an accessible portal, providing up-to-date information and undoubtedly raises the situational awareness of the officers. Arguably, the domain awareness systems that are taking shape in Jharkhand would pave the way for predictive policing methods to be applied in the future. These systems and hot spot maps seem to be the start of a new age of policing in Jharkhand.</p>
<h3 style="text-align: justify; ">Predictive Policing Research</h3>
<p style="text-align: justify; ">One promising idea for predictive policing in India comes from the research conducted by Lavanya Gupta and others entitled "Predicting Crime Rates for Predictive Policing"<a href="#_ftn59" name="_ftnref59">[59]</a>, which was a submission for the Gandhian Young Technological Innovation Award. The research uses regression modelling to predict future crime rates. Drawing from First Information Reports (FIRs) of violent crimes (murder, rape, kidnapping etc.) from Chandigarh Police, the team attempted "to extrapolate annual crime rate trends developed through time series models. This approach also involves correlating past crime trends with factors that will influence the future scope of crime, in particular demographic and macro-economic variables" <a href="#_ftn60" name="_ftnref60">[60]</a>. The researchers used early crime data as the training data for their model, which after some testing, eventually turned out to have an accuracy of around 88.2%.<a href="#_ftn61" name="_ftnref61">[61]</a> On the face of it, ideas like this could be the starting point for the introduction of predictive policing into India.</p>
<p style="text-align: justify; ">The rest of India's law enforcement bodies do not appear to be lagging behind. In the 44<sup>th</sup> All India police science congress, held in Gandhinagar, Gujarat in March this year, one of the Themes for discussion was the "Role of Preventive Forensics and latest developments in Voice Identification, Tele-forensics and Cyber Forensics"<a href="#_ftn62" name="_ftnref62">[62]</a>.Mr A K Singh, (Additional Director General of Police, Administration) the chairman of the event also said in an interview that there was to be a round-table DGs (Director General of Police) held at the conference to discuss predictive policing<a href="#_ftn63" name="_ftnref63">[63]</a>. Perhaps predictive policing in India may not be that far away from reality.</p>
<h3 style="text-align: justify; ">CCTNS and the building blocks of Predictive policing</h3>
<p style="text-align: justify; ">The Ministry of Home Affairs conceived of a Crime and Criminals Tracking and Network System (CCTNS) as part of national e-Governance plans. According to the website of the National Crime Records Bureau (NCRB), CCTNS aims to develop "a nationwide networked infrastructure for evolution of IT-enabled state-of-the-art tracking system around 'investigation of crime and detection of criminals' in real time" <a href="#_ftn64" name="_ftnref64">[64]</a></p>
<p style="text-align: justify; ">The plans for predictive policing seem in the works, but first steps that are needed in India across police forces involve digitizing data collection by the police, as well as connecting law enforcement agencies. The NCRB's website described the current possibility of exchange of information between neighbouring police stations, districts or states as being "next to impossible"<a href="#_ftn65" name="_ftnref65">[65]</a>. The aim of CCTNS is precisely to address this gap and integrate and connect the segregated law enforcement arms of the state in India, which would be a foundational step in any initiatives to apply predictive methods.</p>
<h2 style="text-align: justify; ">What are the implications of using predictive policing? Lessons from USA</h2>
<p style="text-align: justify; ">Despite the moves by law enforcement agencies to adopt predictive policing, one reality is that the implications of predictive policing methods are far from clear. This section will examine these implications on the carriage of justice and its use in law, as well as how it impacts privacy concerns for the individual. It frames the existing debates surrounding these issues with predictive policing, and aims to apply these principles into an Indian context.</p>
<h3 style="text-align: justify; ">Justice, Privacy & IV Amendment</h3>
<p style="text-align: justify; ">Two key concerns about how predictive policing methods may be used by law enforcement relate to how insights from predictive policing methods are acted upon and how courts interpret them. In the USA, this issue may finds its place under the scope of IV Amendment jurisprudence. The IV amendment states that all citizens are "secure from unreasonable searches and seizures of property by the government"<a href="#_ftn66" name="_ftnref66">[66]</a>. In this sense, the IV amendment forms the basis for search and surveillance law in the USA.</p>
<p style="text-align: justify; ">A central aspect of the IV Amendment jurisprudence is drawn from <i>United States v. Katz</i>. In <i>Katz</i>, the FBI attached a microphone to the outside of a public phone booth to record the conversations of Charles Katz, who was making phone calls related to illegal gambling. The court ruled that such actions constituted a search within the auspices of the 4<sup>th</sup> amendment. The ruling affirmed constitutional protection of all areas where someone has a "reasonable expectation of privacy"<a href="#_ftn67" name="_ftnref67">[67]</a>.</p>
<p style="text-align: justify; ">Later cases have provided useful tests for situations where government surveillance tactics may or may not be lawful, depending on whether it violates one's reasonable expectation of privacy. For example, in <i>United States v. Knotts</i>, the court held that "police use of an electronic beeper to follow a suspect surreptitiously did not constitute a Fourth Amendment search"<a href="#_ftn68" name="_ftnref68">[68]</a>. In fact, some argue that that the Supreme Court's reasoning in such cases suggests " any 'scientific enhancement' of the senses used by the police to watch activity falls outside of the Fourth Amendment's protections if the activity takes place in public"<a href="#_ftn69" name="_ftnref69">[69]</a>. This reasoning is based on the third party doctrine which holds that "if you voluntarily provide information to a third party, the IV Amendment does not preclude the government from accessing it without a warrant"<a href="#_ftn70" name="_ftnref70">[70]</a>. The clearest exposition of this reasoning was in Smith v. Maryland, where the presiding judges noted that "this Court consistently has held that a person has no legitimate expectation of privacy in information he voluntarily turns over to third parties"<a href="#_ftn71" name="_ftnref71">[71]</a>.</p>
<p style="text-align: justify; ">However, the third party has seen some challenge in recent time. In <i>United States v. Jones</i>, it was ruled that the government's warrantless GPS tracking of his vehicle 24 hours a day for 28 days violated his Fourth Amendment rights<a href="#_ftn72" name="_ftnref72">[72]</a>. Though the majority ruling was that warrantless GPS tracking constituted a search, it was in a concurring opinion written by Justice Sonya Sotomayor that such intrusive warrantless surveillance was said to infringe one's reasonable expectation of privacy. As Newell reflected on Sotomayor's opinion,</p>
<p style="text-align: justify; ">"Justice Sotomayor stated that the time had come for Fourth Amendment jurisprudence to discard the premise that legitimate expectations of privacy could only be found in situations of near or complete secrecy. Sotomayor argued that people should be able to maintain reasonable expectations of privacy in some information voluntarily disclosed to third parties"<a href="#_ftn73" name="_ftnref73">[73]</a>.</p>
<p style="text-align: justify; ">She said that the court's current reasoning on what constitutes reasonable expectations of privacy in information disclosed to third parties, such as email or phone records or even purchase histories, is "ill-suited to the digital age, in which people reveal a great deal of information about themselves to third parties in the course of carrying out mundane tasks"<a href="#_ftn74" name="_ftnref74">[74]</a>.</p>
<h3 style="text-align: justify; ">Predictive policing vs. Mass surveillance and Domain Awareness Systems</h3>
<p style="text-align: justify; ">However, there is an important distinction to be drawn between these cases and evidence from predictive policing. This has to do with the difference in nature of the evidence collection. Arguably, from Jones and others, what we see is that use of mass surveillance and domain awareness systems, drawing from Joh's categorisation of domain awareness systems as being distinct from predictive policing mentioned above, could potentially encroach on one's reasonable expectation of privacy. However, I think that predictive policing, and the possible implications for justice associated with it, its predictive harms, are quite distinct from what has been heard by courts thus far.</p>
<p style="text-align: justify; ">The reason for distinct risks between predictive harms and privacy harms originating from information gathering is related to the nature of predictive policing technologies, and how they are used. It is highly unlikely that the evidence submitted by the State to indict an offender will be mainly predictive in nature. For example, would it be possible to convict an accused person solely on the premise that he was predicted to be highly likely to commit a crime, and that subsequently he did? The legal standard of proving guilt beyond a reasonable doubt <a href="#_ftn75" name="_ftnref75">[75]</a> can hardly be met solely on predictive evidence for a multitude of reasons. Predictive policing methods could at most, be said to inform police about the risk of someone committing a crime or of crime happening at a certain location, as demonstrated above.</p>
<h4 style="text-align: justify; ">Predictive policing and Criminal Procedure</h4>
<p style="text-align: justify; ">It may therefore pay to analyse how predictive policing may be used across the various processes within the criminal justice system. In fact, in an analysis of the various stages of criminal procedure, from opening an investigation to gathering evidence, followed by arrest, trial, conviction and sentencing, we see that as the individual gets subject to more serious incursions or sanctions by the state, it takes a higher standard of certainty about wrongdoing and a higher burden of proof, in order to legitimize that particular action.</p>
<p style="text-align: justify; ">Hence, at more advanced stages of the criminal justice process such as seeking arrest warrants or trial, it is very unlikely that predictive policing on its own can have a tangible impact, because the nature of predictive evidence is probability based. It aims to calculate the risk of future crime occurring based on statistical analysis of past crime data<a href="#_ftn76" name="_ftnref76">[76]</a>. While extremely useful, probabilities on their own will not come remotely close meet the legal standards of proving 'guilt beyond reasonable doubt'. It may be at the earlier stages of the criminal justice process that evidence predictive policing might see more widespread application, in terms of applying for search warrants and searching suspicious people while on patrol.</p>
<p style="text-align: justify; ">In fact, in the law enforcement context, prediction as a concept is not new to justice. Both courts and law enforcement officials already make predictions about future likelihood of crimes. In the case of issuing warrants, the IV amendment makes provisions that law enforcement officials show that the potential search is based "upon probable cause"<a href="#_ftn77" name="_ftnref77">[77]</a> in order for a judge to grant a warrant. In <i>US v. Brinegar</i>, probable cause was defined as existing "where the facts and circumstances within the officers' knowledge, and of which they have reasonably trustworthy information, are sufficient in themselves to warrant a belief by a man of reasonable caution that a crime is being committed" <a href="#_ftn78" name="_ftnref78">[78]</a>. Again, this legal standard seems too high for predictive evidence meet.</p>
<p style="text-align: justify; ">However, the police also have an important role to play in preventing crimes by looking out for potential crimes while on patrol or while doing surveillance. When the police stop a civilian on the road to search him, reasonable suspicion must be established. This standard of reasonable suspicion was defined in most clearly in <i>Terry v. Ohio</i>, which required police to "be able to point to specific and articulable facts which, taken together with rational inferences from those facts, reasonably warrant that intrusion"<a href="#_ftn79" name="_ftnref79">[79]</a>. Therefore, "reasonable suspicion that 'criminal activity may be afoot' is at base a prediction that the facts and circumstances warrant the reasonable prediction that a crime is occurring or will occur"<a href="#_ftn80" name="_ftnref80">[80]</a>. Despite the assertion that "there are as of yet no reported cases on predictive policing in the Fourth Amendment context"<a href="#_ftn81" name="_ftnref81">[81]</a>, examining the impact of predictive policing on the doctrine of reasonable suspicion could be very instructive in understanding the implications for justice and privacy <a href="#_ftn82" name="_ftnref82">[82]</a>.</p>
<h3 style="text-align: justify; ">Predictive Policing and Reasonable Suspicion</h3>
<p style="text-align: justify; ">Ferguson's insightful contribution to this area of scholarship involves the identification of existing areas where prediction already takes place in policing, and analogising them into a predictive policing context<a href="#_ftn83" name="_ftnref83">[83]</a>. These three areas are: responding to tips, profiling, and high crime areas (hot spots).</p>
<h4 style="text-align: justify; ">Tips</h4>
<p style="text-align: justify; ">Tips are pieces of information shared with the police by members of the public. Often tips, either anonymous or from known police informants, may predict future actions of certain people, and require the police to act on this information. The precedent for understanding the role of tips in probable cause comes from <i>Illinois v. Gates</i><a href="#_ftn84" name="_ftnref84">[84]</a>. It was held that "an informant's 'veracity,' 'reliability,' and 'basis of knowledge'-remain 'highly relevant in determining the value'"<a href="#_ftn85" name="_ftnref85">[85]</a> of the said tip. Anonymous tips need to be detailed, timely and individualised enough<a href="#_ftn86" name="_ftnref86">[86]</a> to justify reasonable suspicion <a href="#_ftn87" name="_ftnref87">[87]</a>. And when the informant is known to be reliable, then his prior reliability may justify reasonable suspicion despite lacking a basis in knowledge<a href="#_ftn88" name="_ftnref88">[88]</a>.</p>
<p style="text-align: justify; ">Ferguson argues that whereas predictive policing cannot provide individualised tips, it is possible to consider reliable tips about certain areas as a parallel to predictive policing<a href="#_ftn89" name="_ftnref89">[89]</a>. And since the courts had shown a preference for reliability even in the face of a weak basis in knowledge, it is possible to see the reasonable suspicion standard change in its application<a href="#_ftn90" name="_ftnref90">[90]</a>. It also implies that IV protections may be different in places where crime is predicted to occur <a href="#_ftn91" name="_ftnref91">[91]</a>.</p>
<h4 style="text-align: justify; ">Profiling</h4>
<p style="text-align: justify; ">Despite the negative connotations and controversial overtones at the mere sound of the word, profiling is already a method commonly used by law enforcement. For example, after a crime has been committed and general features of the suspect identified by witnesses, police often stop civilians who fit this description. Another example of profiling is common in combating drug trafficking<a href="#_ftn92" name="_ftnref92">[92]</a>, where agents keep track of travellers at airports to watch for suspicious behaviour. Based on their experience of common traits which distinguish drug traffickers from regular travellers (a profile), agents may search travellers if they fit the profile<a href="#_ftn93" name="_ftnref93">[93]</a>. In the case of <i>United States v. Sokolow</i><a href="#_ftn94" name="_ftnref94">[94]</a>, the courts "recognized that a drug courier profile is not an irrelevant or inappropriate consideration that, taken in the totality of circumstances, can be considered in a reasonable suspicion determination" <a href="#_ftn95" name="_ftnref95">[95]</a>. Similar lines of thinking could be employed in observing people exchanging small amounts of money in an area known for high levels of drug activity, conceiving predictive actions as a form of profile<a href="#_ftn96" name="_ftnref96">[96]</a>.</p>
<p style="text-align: justify; ">It is valid to consider predictive policing as a form of profiling<a href="#_ftn97" name="_ftnref97">[97]</a>, but Ferguson argues that the predictive policing context means this 'new form' of profiling could change IV analysis. The premise behind such an argument lies in the fact that a prediction made by some algorithm about potential high risk of crime in a certain area, could be taken in conjunction observations of ordinarily innocuous events. Read in the totality of circumstances, these two threads may justify individual reasonable suspicion <a href="#_ftn98" name="_ftnref98">[98]</a>. For example, a man looking into cars at a parking lot may not by itself justify reasonable suspicion, but taken together with a prediction of high risk of car theft at that locality, it may well justify reasonable suspicion. It is this impact of predictive policing, which influences the analysis of reasonable suspicion in a totality of circumstances that may represent new implications for courts looking at IV amendment protections.</p>
<h5 style="text-align: justify; ">Profiling, Predictive Policing and Discrimination</h5>
<p style="text-align: justify; ">The above sections have already brought up the point that law enforcement agencies already utilize profiling methods in their operations. Also, as the sections on how predictive analytics works and on methods of predictive policing make clear, predictive policing definitely incorporates the development of profiles for predicting future criminal activity. Concerns about predictive models generate potentially discriminatory predictions therefore are very serious, and need addressing. Potential discrimination may be either overt, though far less likely, or unintended. A valuable case study of which sheds light on such discriminatory data mining practices can be found in US Labour law. It was shown how predictive models could be discriminatory at various stages, from conceptualising the model and training it with training data, to eventually selecting inappropriate features to search for <a href="#_ftn99" name="_ftnref99">[99]</a>. It is also possible for data scientists to (intentionally or not) use proxies for identifiers like race, income level, health condition and religion. Barocas and Selbst argue that "the current distribution of relevant attributes-attributes that can and should be taken into consideration in apportioning opportunities fairly-are demonstrably correlated with sensitive attributes" <a href="#_ftn100" name="_ftnref100">[100]</a>. Hence, what may result is unintended discrimination, as predictive models and their subjective and implicit biases are reflected in predicted decisions, or that the discrimination is not even accounted for in the first place. While I have not found any case law where courts have examined such situations in a criminal context, at the very least, law enforcement agencies need to be aware of these possibilities and guard against any forms of discriminatory profiling.</p>
<p style="text-align: justify; ">However, Ferguson argues that "the precision of the technology may in fact provide more protection for citizens in broadly defined high crime areas" <a href="#_ftn101" name="_ftnref101">[101]</a>. This is because the label of a 'high-crime area' may no longer apply to large areas but instead to very specific areas of criminal activity. This implies that previously defined areas of high crime, like entire neighbourhoods may not be scrutinised in such detail. Instead, police now may be more precise in locating and policing areas of high crime, such as an individual street corner or a particular block of flats instead of an entire locality.</p>
<h4 style="text-align: justify; ">Hot Spots</h4>
<p style="text-align: justify; ">Courts have also considered the existence of notoriously 'high-crime areas as part of considering reasonable suspicion<a href="#_ftn102" name="_ftnref102">[102]</a>. This was seen in <i>Illinois v. Wardlow</i> <a href="#_ftn103" name="_ftnref103">[103]</a>, where the "high crime nature of an area can be considered in evaluating the officer's objective suspicion"<a href="#_ftn104" name="_ftnref104">[104]</a>. Many cases have since applied this reasoning without scrutinising the predictive value of such a label. In fact, Ferguson asserts that such labelling has questionable evidential value<a href="#_ftn105" name="_ftnref105">[105]</a>. He uses the facts of the <i>Wardlow </i>case itself to challenge the 'high crime area' factor. Ferguson cites the reasoning of one of the judges in the case:</p>
<p style="text-align: justify; ">"While the area in question-Chicago's District 11-was a low-income area known for violent crimes, how that information factored into a predictive judgment about a man holding a bag in the afternoon is not immediately clear."<a href="#_ftn106" name="_ftnref106">[106]</a></p>
<p style="text-align: justify; ">Especially because "the most basic models of predictive policing rely on past crimes"<a href="#_ftn107" name="_ftnref107">[107]</a>, it is likely that the predictive policing methods like hot spot or spatiotemporal analysis and risk terrain modelling may help to gather or build data models about high crime areas. Furthermore, the mathematical rigour of the predictive modelling could help clarify the term 'high crime area'. As Ferguson argues, "courts may no longer need to rely on the generalized high crime area terminology when more particularized and more relevant information is available" <a href="#_ftn108" name="_ftnref108">[108]</a>.</p>
<h4 style="text-align: justify; ">Summary</h4>
<p style="text-align: justify; ">Ferguson synthesises four themes to which encapsulate reasonable suspicion analysis:</p>
<ol>
<li> Predictive information is not enough on its own. Instead, it is "considered relevant to the totality of circumstances, but must be corroborated by direct police observation"<a href="#_ftn109" name="_ftnref109">[109]</a>.</li>
<li>The prediction must also "be particularized to a person, a profile, or a place, in a way that directly connects the suspected crime to the suspected person, profile, or place"<a href="#_ftn110" name="_ftnref110">[110]</a>.</li>
<li>It must also be detailed enough to distinguish a person or place from others not the focus of the prediction <a href="#_ftn111" name="_ftnref111">[111]</a>.</li>
<li>Finally, predicted information becomes less valuable over time. Hence it must be acted on quickly or be lost <a href="#_ftn112" name="_ftnref112">[112]</a>.</li>
</ol>
<h4 style="text-align: justify; ">Conclusions from America</h4>
<p style="text-align: justify; ">The main conclusion to draw from the analysis of the parallels between existing predictions in IV amendment law and predictive policing is that "predictive policing will impact the reasonable suspicion calculus by becoming a factor within the totality of circumstances test"<a href="#_ftn113" name="_ftnref113">[113]</a>. Naturally, it reaffirms the imperative for predictive techniques to collect reliable data <a href="#_ftn114" name="_ftnref114">[114]</a> and analyse it transparently<a href="#_ftn115" name="_ftnref115">[115]</a>. Moreover, in order for courts to evaluate the reliability of the data and the processes used (since predictive methods become part of the reasonable suspicion calculus), courts need to be able to analyse the predictive process. This has implications for the how hearings may be conducted, for how legal adjudicators may require training and many more. Another important concern is that the model of predictive information and police corroboration or direct observation<a href="#_ftn116" name="_ftnref116">[116]</a> may mean that in areas which were predicted to have low risk of crime, the reasonable suspicion doctrine works against law enforcement. There may be less effort paid to patrolling these other areas as a result of predictions.</p>
<h2 style="text-align: justify; ">Implications for India</h2>
<p style="text-align: justify; ">While there have been no cases directly involving predictive policing methods, it would be prudent to examine the parts of Indian law which would inform the calculus on the lawfulness of using predictive policing methods. A useful lens to examine this might be found in the observation that prediction is not in itself a novel concept in justice, and is already used by courts and law enforcement in numerous circumstances.</p>
<h3 style="text-align: justify; ">Criminal Procedure in Non-Warrant Contexts</h3>
<p style="text-align: justify; ">The most logical way to begin analysing the legal implications of predictive policing in India may probably involve identifying parallels between American and Indian criminal procedure, specifically searching for instances where 'reasonable suspicion' or some analogous requirement exists for justifying police searches.</p>
<p style="text-align: justify; ">In non-warrant scenarios, we find conditions for officers to conduct such a warrantless search in Section 165 of the Criminal Procedure Code (Cr PC). For clarity purposes I have stated section 165 (1) in full:</p>
<p style="text-align: justify; ">"Whenever an officer in charge of a police station or a police officer making an investigation <b>has reasonable grounds</b> for believing that anything necessary for the purposes of an investigation into any offence which he is authorised to investigate may be found in any place with the limits of the police station of which he is in charge, or to which he is attached, and that such thing cannot in his opinion be otherwise obtained without undue delay, such officer may, after recording in writing the grounds of his belief and specifying in such writing, so far as possible, the thing for which search is to be made, search, or cause search to be made, for such thing in any place within the limits of such station." <a href="#_ftn117" name="_ftnref117">[117]</a></p>
<p style="text-align: justify; ">However, India differs from the USA in that its Cr PC allows for police to arrest individuals without a warrant as well. As observed in <i>Gulab Chand Upadhyaya vs State Of U.P</i>, "Section 41 Cr PC gives the power to the police to arrest without warrant in cognizable offences, in cases enumerated in that Section. One such case is of receipt of a 'reasonable complaint' or 'credible information' or 'reasonable suspicion'" <a href="#_ftn118" name="_ftnref118">[118]</a> Like above, I have stated section 41 (1) and subsection (a) in full:</p>
<p style="text-align: justify; ">"41. When police may arrest without warrant.</p>
<p style="text-align: justify; "><a href="http://indiankanoon.org/doc/507354/">(1)</a> Any police officer may without an order from a Magistrate and without a warrant, arrest any person-</p>
<p style="text-align: justify; "><a href="http://indiankanoon.org/doc/1315149/">(a)</a> who has been concerned in any cognizable offence, or against whom a <b>reasonable complaint has been made, or credible information has been received, or a reasonable suspicion exists</b>, of his having been so concerned"<a href="#_ftn119" name="_ftnref119">[119]</a></p>
<p style="text-align: justify; ">In analysing the above sections of Indian criminal procedure from a predictive policing angle, one may find both similarities and differences between the proposed American approach and possible Indian approaches to interpreting or incorporating predictive policing evidence.</p>
<h4 style="text-align: justify; ">Similarity of 'reasonable suspicion' requirement</h4>
<p style="text-align: justify; ">For one, the requirement for "reasonable grounds" or "reasonable suspicion" seems to be analogous to the American doctrine of reasonable suspicion. This suggests that the concepts used in forming reasonable suspicion, for the police to "be able to point to specific and articulable facts which, taken together with rational inferences from those facts, reasonably warrant that intrusion"<a href="#_ftn120" name="_ftnref120">[120]</a> may also be useful in the Indian context.</p>
<p style="text-align: justify; ">One case which sheds light on an Indian interpretation of reasonable suspicion or grounds is <i>State of Punjab v. Balbir Singh<a href="#_ftn121" name="_ftnref121"><b>[121]</b></a></i>. In that case, the court observes a requirement for "reason to believe that such an offence under Chapter IV has been committed and, therefore, an arrest or search was necessary as contemplated under these provisions"<a href="#_ftn122" name="_ftnref122">[122]</a> in the context of Section 41 and 42 in The Narcotic Drugs and Psychotropic Substances Act, 1985<a href="#_ftn123" name="_ftnref123">[123]</a>. In examining the requirement of having "reason to believe", the court draws on <i>Partap Singh (Dr)</i> v. <i>Director of Enforcement, Foreign Exchange Regulation Act<a href="#_ftn124" name="_ftnref124"><b>[124]</b></a></i>, where the judge observed that "the expression 'reason to believe' is not synonymous with subjective satisfaction of the officer. The belief must be held in good faith; it cannot be merely a pretence….."<a href="#_ftn125" name="_ftnref125">[125]</a></p>
<p style="text-align: justify; ">In light of this, the judge in <i>Balbir Singh </i>remarked that "whether there was such reason to believe and whether the officer empowered acted in a bona fide manner, depends upon the facts and circumstances of the case and will have a bearing in appreciation of the evidence" <a href="#_ftn126" name="_ftnref126">[126]</a>. The standard considered by the court in <i>Balbir Singh </i>and <i>Partap Singh</i> is different from the 'reasonable suspicion' or 'reasonable grounds' standard as per Section 41 and 165 of Cr PC. But I think the discussion can help to inform our analysis of the idea of reasonableness in law enforcement actions. Of importance was the court requirement of something more than mere "pretence" as well as a belief held in good faith. This could suggest that in fact the reasoning in American jurisprudence about reasonable suspicion might be at least somewhat similar to how Indian courts view reasonable suspicion or grounds in the context of predictive policing, and therefore how we could similarly conjecture that predictive evidence could form part of the reasonable suspicion calculus in India as well.</p>
<h4 style="text-align: justify; ">Difference in judicial treatment of illegally obtained evidence - Indian lack of exclusionary rules</h4>
<p style="text-align: justify; ">However, the apparent similarity of how police in America and India may act in non-warrant situations - guided by the idea of reasonable suspicion - is only veneered by linguistic parallels. Despite the existence of such conditions which govern the searches without a warrant, I believe that Indian courts currently may provide far less protection against unlawful use of predictive technologies. The main premise behind this argument is that Indian courts refuse to exclude evidence that was obtained in breaches of the conditions of sections of the Cr PC. What exists in place of evidentiary safeguards is a line of cases in which courts routinely admit unlawfully or illegally obtained evidence. Without protections against unlawfully gathered evidence being considered relevant by courts, any regulations on search or conditions to be met before a search is lawful become ineffective. Evidence may simply enter the courtroom through a backdoor.</p>
<p style="text-align: justify; ">In the USA, this is by and large, not the case. Although there are exceptions to these rules, exclusionary rules are set out to prevent admission of evidence which violates the constitution<a href="#_ftn127" name="_ftnref127">[127]</a>. "The exclusionary rule applies to evidence gained from an unreasonable search or seizure in violation of the Fourth Amendment "<a href="#_ftn128" name="_ftnref128">[128]</a>. Mapp v. Ohio <a href="#_ftn129" name="_ftnref129">[129]</a> set the precedent for excluding unconstitutionally gathered evidence, where the court ruled that "all evidence obtained by searches and seizures in violation of the Federal Constitution is inadmissible in a criminal trial in a state court" <a href="#_ftn130" name="_ftnref130">[130]</a>.</p>
<p style="text-align: justify; ">Any such evidence which then leads law enforcement to collect new information may also be excluded, as part of the "fruit of the poisonous tree" doctrine<a href="#_ftn131" name="_ftnref131">[131]</a>, established in Silverthorne Lumber Co. v. United States <a href="#_ftn132" name="_ftnref132">[132]</a>. The doctrine is a metaphor which suggests that if the source of certain evidence is tainted, so is 'fruit' or derivatives from that unconstitutional evidence. One such application was in <i>Beck v. Ohio<a href="#_ftn133" name="_ftnref133"><b>[133]</b></a></i>, where the courts overturned a petitioner's conviction because the evidence used to convict him was obtained via an unlawful arrest.</p>
<p style="text-align: justify; ">However in India's context, there is very little protection against the admission and use of unlawfully gathered evidence. In fact, there are a line of cases which lay out the extent of consideration given to unlawfully gathered evidence - both cases that specifically deal with the rules as per the Indian Cr PC as well as cases from other contexts - which follow and develop this line of reasoning of allowing illegally obtained evidence.</p>
<p style="text-align: justify; ">One case to pay attention to is <i>State of Maharastra v. Natwarlal Damodardas Soni</i> - in this case, the Anti-Corruption Bureau searched the house of the accused after receiving certain information as a tip. The police "had powers under the Code of Criminal Procedure to search and seize this gold if they had reason to believe that a cognizable offence had been committed in respect thereof"<a href="#_ftn134" name="_ftnref134">[134]</a>. Justice Sarkaria, in delivering his judgement, observed that for argument's sake, even if the search was illegal, "then also, it will not affect the validity of the seizure and further investigation"<a href="#_ftn135" name="_ftnref135">[135]</a>. The judge drew reasoning from <i>Radhakishan v. State of U.P</i><a href="#_ftn136" name="_ftnref136">[136]</a>. This which was a case involving a postman who had certain postal items that were undelivered recovered from his house. As the judge in <i>Radhakishan</i> noted:</p>
<p style="text-align: justify; ">"So far as the alleged illegality of the search is concerned, it is sufficient to say that even assuming that the search was illegal the seizure of the articles is not vitiated. It may be that where the provisions of Sections 103 and 165 of the Code of Criminal Procedure, are contravened the search could be resisted by the person whose premises are sought to be searched. It may also be that because of the illegality of the search the court may be inclined to examine carefully the evidence regarding the seizure. But beyond these two consequences no further consequence ensues." <a href="#_ftn137" name="_ftnref137">[137]</a></p>
<p style="text-align: justify; "><i>Shyam Lal Sharma</i> v. <i>State of M.P.<a href="#_ftn138" name="_ftnref138"><b>[138]</b></a></i> was also drawn upon, where it was held that "even if the search is illegal being in contravention with the requirements of Section 165 of the Criminal Procedure Code, 1898, that provision ceases to have any application to the subsequent steps in the investigation"<a href="#_ftn139" name="_ftnref139">[139]</a>.</p>
<p style="text-align: justify; ">Even in <i>Gulab Chand </i><i>Upadhyay</i>, mentioned above, the presiding judge contended that even "if arrest is made, it does not require any, much less strong, reasons to be recorded or reported by the police. Thus so long as the information or suspicion of cognizable offence is "reasonable" or "credible", the police officer is not accountable for the discretion of arresting or no arresting"<a href="#_ftn140" name="_ftnref140">[140]</a>.</p>
<p style="text-align: justify; ">A more complete articulation of the receptiveness of Indian courts to admit illegally gathered evidence can be seen in the aforementioned <i>Balbir Singh. </i>The judgement aimed to:</p>
<p style="text-align: justify; ">"dispose of one of the contentions that failure to comply with the provisions of Cr PC in respect of search and seizure even up to that stage would also vitiate the trial. This aspect has been considered in a number of cases and it has been held that the violation of the provisions particularly that of Sections 100, 102, 103 or 165 Cr PC strictly per se does not vitiate the prosecution case. If there is such violation, what the courts have to see is whether any prejudice was caused to the accused and in appreciating the evidence and other relevant factors, the courts should bear in mind that there was such a violation and from that point of view evaluate the evidence on record."<a href="#_ftn141" name="_ftnref141">[141]</a></p>
<p style="text-align: justify; ">The judges then consulted a series of authorities on the failure to comply with provisions of the Cr PC:</p>
<ol>
<li><i>State of Punjab</i> v. <i>Wassan Singh</i><a href="#_ftn142" name="_ftnref142">[142]</a><i>:</i> "irregularity in a search cannot vitiate the seizure of the articles"<a href="#_ftn143" name="_ftnref143">[143]</a>.</li>
<li style="text-align: justify; "><i>Sunder Singh</i> v. <i>State of U.P</i><a href="#_ftn144" name="_ftnref144">[144]</a><i>:</i> 'irregularity cannot vitiate the trial unless the accused has been prejudiced by the defect and it is also held that if reliable local witnesses are not available the search would not be vitiated."<a href="#_ftn145" name="_ftnref145">[145]</a></li>
<li style="text-align: justify; "><i>Matajog Dobey</i> v.<i>H.C. Bhari</i><a href="#_ftn146" name="_ftnref146">[146]</a><i>:</i> "when the salutory provisions have not been complied with, it may, however, affect the weight of the evidence in support of the search or may furnish a reason for disbelieving the evidence produced by the prosecution unless the prosecution properly explains such circumstance which made it impossible for it to comply with these provisions."<a href="#_ftn147" name="_ftnref147">[147]</a></li>
<li style="text-align: justify; "><i>R</i> v. <i>Sang</i><a href="#_ftn148" name="_ftnref148">[148]</a>: "reiterated the same principle that if evidence was admissible it matters not how it was obtained."<a href="#_ftn149" name="_ftnref149">[149]</a> Lord Diplock, one of the Lords adjudicating the case, observed that "however much the judge may dislike the way in which a particular piece of evidence was obtained before proceedings were commenced, if it is admissible evidence probative of the accused's guilt "it is no part of his judicial function to exclude it for this reason". <a href="#_ftn150" name="_ftnref150">[150]</a> As the judge in <i>Balbir Singh</i> quoted from Lord Diplock, a judge "has no discretion to refuse to admit relevant admissible evidence on the ground that it was obtained by improper or unfair means. The court is not concerned with how it was obtained."<a href="#_ftn151" name="_ftnref151">[151]</a></li>
</ol>
<p style="text-align: justify; ">The vast body of case law presented above provides observers with a clear image of the courts willingness to admit and consider illegally obtained evidence. The lack of safeguards against admission of unlawful evidence are important from the standpoint of preventing the excessive or unlawful use of predictive policing methods. The affronts to justice and privacy, as well as the risks of profiling, seem to become magnified when law enforcement use predictive methods more than just to augment their policing techniques but to replace some of them. The efficacy and expediency offered by using predictive policing needs to be balanced against the competing interest of ensuring rule of law and due process. In the Indian context, it seems courts sparsely consider this competing interest.</p>
<p style="text-align: justify; ">Naturally, weighing in on which approach is better depends on a multitude of criteria like context, practicality, societal norms and many more. It also draws on existing debates in administrative law about the role of courts, which may emphasise protecting individuals and preventing excessive state power (red light theory) or emphasise efficiency in the governing process with courts assisting the state to achieve policy objectives (green light theory) <a href="#_ftn152" name="_ftnref152">[152]</a>.</p>
<p style="text-align: justify; ">A practical response may be that India should aim to embrace both elements and balance them appropriately, although what an appropriate balance again may vary. There are some who claim that this balance already exists in India. Evidence for such a claim may come from <i>R.M. Malkani v. State of Maharashtra</i><a href="#_ftn153" name="_ftnref153">[153]</a>, where the court considered whether an illegally tape-recorded conversation<i> </i>could be admissible. In its reasoning, the court drew from <i>Kuruma, Son of Kanju v. R.</i> <a href="#_ftn154" name="_ftnref154">[154]</a><i>, </i>noting that</p>
<p style="text-align: justify; "><i>"</i> if evidence was admissible it matters not how it was obtained. There is of course always a word of caution. It is that the Judge has a discretion to disallow evidence in a criminal case if the strict rules of admissibility would operate unfairly against the accused. That caution is the golden rule in criminal jurisprudence"<a href="#_ftn155" name="_ftnref155">[155]</a>.</p>
<p style="text-align: justify; ">While this discretion exists at least principally in India, in practice the cases presented above show that judges rarely exercise that discretion to prevent or bar the admission of illegally obtained evidence or evidence that was obtained in a manner that infringed the provisions governing search or arrest in the Cr PC. Indeed, the concern is that perhaps the necessary safeguards required to keep law enforcement practices, including predictive policing techniques, in check would be better served by a greater focus on reconsidering the legality of unlawfully gathered evidence. If not, evidence which should otherwise be inadmissible may find its way into consideration by existing legal backdoors.</p>
<h3 style="text-align: justify; ">Risk of discriminatory predictive analysis</h3>
<p style="text-align: justify; ">Regarding the risk of discriminatory profiling, Article 15 of India's Constitution<a href="#_ftn156" name="_ftnref156">[156]</a> states that "the State shall not discriminate against any citizen on grounds only of religion, race, caste, sex, place of birth or any of them" <a href="#_ftn157" name="_ftnref157">[157]</a>. The existence of constitutional protection for such forms of discrimination suggests that India will be able to guard against discriminatory predictive policing. However, as mentioned before, predictive analytics often discriminates institutionally, "whereby unconscious implicit biases and inertia within society's institutions account for a large part of the disparate effects observed, rather than intentional choices"<a href="#_ftn158" name="_ftnref158">[158]</a>. As in most jurisdictions, preventing these forms of discrimination are much harder. Especially in a jurisdiction whose courts are already receptive to allowing admission of illegally obtained evidence, the risk of discriminatory data mining or prejudiced algorithms being used by police becomes magnified. Because the discrimination may be unintentional, it may be even harder for evidence from discriminatory predictive methods to be scrutinised or when applicable, dismissed by the courts.</p>
<h2 style="text-align: justify; ">Conclusion for India</h2>
<p style="text-align: justify; ">One thing which is eminently clear from the analysis of possible interpretations of predictive evidence is that Indian Courts have had no experience with any predictive policing cases, because the technology itself is still at a nascent stage. There is in fact a long way to go before predictive policing will become used on a scale similar to that of USA for example.</p>
<p style="text-align: justify; ">But, even in places where predictive policing is used much more prominently, there is no precedent to observe how courts may view predictive policing. Ferguson's method of locating analogous situations to predictive policing which courts have already considered is one notable approach, but even this does not provide complete answer. One of his main conclusions that predictive policing will affect the reasonable suspicion calculus, or in India's case, contribute to 'reasonable grounds' in some ways, is perhaps the most valid one.</p>
<p style="text-align: justify; ">However, what provides more cause for concern in India's context are the limited protections against use of unlawfully gathered evidence. The lack of 'exclusionary rules' unlike those present in the US amplifies the various risks of predictive policing because individuals have little means of redress in such situations where predictive policing may be used unjustly against them.</p>
<p style="text-align: justify; ">Yet, the promise of predictive policing remains undeniably attractive for India. The successes predictive policing methods seem to have had In the US and UK coupled with the more efficient allocation of law enforcement's resources as a consequence of adapting predictive policing evidence this point. The government recognises this and seems to be laying the foundation and basic digital infrastructure required to utilize predictive policing optimally. One ought also to ask whether it is the even within the court's purview to decide what kind of policing methods are to be permissible through evaluating the nature of evidence. There is a case to be made for the legislative arm of the state to provide direction on how predictive policing is to be used in India. Perhaps the law must also evolve with the changes in technology, especially if courts are to scrutinise the predictive policing methods themselves.</p>
<div style="text-align: justify; ">
<hr />
<div id="ftn1">
<p><a href="#_ftnref1" name="_ftn1">[1]</a> Joh, Elizabeth E. "Policing by Numbers: Big Data and the Fourth Amendment." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, February 1, 2014. http://papers.ssrn.com/abstract=2403028. <br /> <br /></p>
</div>
<div id="ftn2">
<p><a href="#_ftnref2" name="_ftn2">[2]</a> Tene, Omer, and Jules Polonetsky. "Big Data for All: Privacy and User Control in the Age of Analytics." Northwestern Journal of Technology and Intellectual Property 11, no. 5 (April 17, 2013): 239.</p>
</div>
<div id="ftn3">
<p><a href="#_ftnref3" name="_ftn3">[3]</a> Datta, Rajbir Singh. "Predictive Analytics: The Use and Constitutionality of Technology in Combating Homegrown Terrorist Threats." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 1, 2013. http://papers.ssrn.com/abstract=2320160.</p>
</div>
<div id="ftn4">
<p><a href="#_ftnref4" name="_ftn4">[4]</a> Johnson, Jeffrey Alan. "Ethics of Data Mining and Predictive Analytics in Higher Education." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 8, 2013. http://papers.ssrn.com/abstract=2156058.</p>
</div>
<div id="ftn5">
<p><a href="#_ftnref5" name="_ftn5">[5]</a> Ibid.</p>
</div>
<div id="ftn6">
<p><a href="#_ftnref6" name="_ftn6">[6]</a> Duhigg, Charles. "How Companies Learn Your Secrets." The New York Times, February 16, 2012. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.</p>
</div>
<div id="ftn7">
<p><a href="#_ftnref7" name="_ftn7">[7]</a> Ibid.</p>
</div>
<div id="ftn8">
<p><a href="#_ftnref8" name="_ftn8">[8]</a> Lijaya, A, M Pranav, P B Sarath Babu, and V R Nithin. "Predicting Movie Success Based on IMDB Data." International Journal of Data Mining Techniques and Applications 3 (June 2014): 365-68.</p>
</div>
<div id="ftn9">
<p><a href="#_ftnref9" name="_ftn9"></a></p>
<p>[9] Johnson, Jeffrey Alan. "Ethics of Data Mining and Predictive Analytics in Higher Education." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, May 8, 2013. http://papers.ssrn.com/abstract=2156058.</p>
</div>
<div id="ftn10">
<p><a href="#_ftnref10" name="_ftn10">[10]</a> Sangvinatsos, Antonios A. "Explanatory and Predictive Analysis of Corporate Bond Indices Returns." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, June 1, 2005. http://papers.ssrn.com/abstract=891641.</p>
</div>
<div id="ftn11">
<p><a href="#_ftnref11" name="_ftn11">[11]</a> Barocas, Solon, and Andrew D. Selbst. "Big Data's Disparate Impact." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, February 13, 2015. http://papers.ssrn.com/abstract=2477899.</p>
</div>
<div id="ftn12">
<p><a href="#_ftnref12" name="_ftn12">[12]</a> Joh, supra note 1.</p>
</div>
<div id="ftn13">
<p><a href="#_ftnref13" name="_ftn13">[13]</a> US Environmental Protection Agency. "How We Use Data in the Mid-Atlantic Region." US EPA. Accessed November 6, 2015. http://archive.epa.gov/reg3esd1/data/web/html/.</p>
</div>
<div id="ftn14">
<p><a href="#_ftnref14" name="_ftn14">[14]</a> See <a href="http://web.archive.org/web/20060603014844/http:/blog.wired.com/27BStroke6/att_klein_wired.pdf">here</a> for details of blackroom.</p>
</div>
<div id="ftn15">
<p><a href="#_ftnref15" name="_ftn15">[15]</a> Joh, supra note 1, at pg 48.</p>
</div>
<div id="ftn16">
<p><a href="#_ftnref16" name="_ftn16">[16]</a> Perry, Walter L., Brian McInnis, Carter C. Price, Susan Smith and John S. Hollywood. Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. Santa Monica, CA: RAND Corporation, 2013. http://www.rand.org/pubs/research_reports/RR233. Also available in print form.</p>
</div>
<div id="ftn17">
<p><a href="#_ftnref17" name="_ftn17">[17]</a> Ibid, at pg 2.</p>
</div>
<div id="ftn18">
<p><a href="#_ftnref18" name="_ftn18">[18]</a> Chan, Sewell. "Why Did Crime Fall in New York City?" City Room. Accessed November 6, 2015. http://cityroom.blogs.nytimes.com/2007/08/13/why-did-crime-fall-in-new-york-city/.</p>
</div>
<div id="ftn19">
<p><a href="#_ftnref19" name="_ftn19">[19]</a> Bureau of Justice Assistance. "COMPSTAT: ITS ORIGINS, EVOLUTION, AND FUTURE IN LAW ENFORCEMENT AGENCIES," 2013. http://www.policeforum.org/assets/docs/Free_Online_Documents/Compstat/compstat%20-%20its%20origins%20evolution%20and%20future%20in%20law%20enforcement%20agencies%202013.pdf.</p>
</div>
<div id="ftn20">
<p><a href="#_ftnref20" name="_ftn20">[20]</a> 1996 internal NYPD article "Managing for Results: Building a Police Organization that Dramatically Reduces Crime, Disorder, and Fear."</p>
</div>
<div id="ftn21">
<p><a href="#_ftnref21" name="_ftn21">[21]</a> Bratton, William. "Crime by the Numbers." The New York Times, February 17, 2010. http://www.nytimes.com/2010/02/17/opinion/17bratton.html.</p>
</div>
<div id="ftn22">
<p><a href="#_ftnref22" name="_ftn22">[22]</a> RAND CORP, supra note 16.</p>
</div>
<div id="ftn23">
<p><a href="#_ftnref23" name="_ftn23">[23]</a> RAND CORP, supra note 16, at pg 19.</p>
</div>
<div id="ftn24">
<p><a href="#_ftnref24" name="_ftn24">[24]</a> Joh, supra note 1, at pg 44.</p>
</div>
<div id="ftn25">
<p><a href="#_ftnref25" name="_ftn25">[25]</a> RAND CORP, supra note 16, pg 38.</p>
</div>
<div id="ftn26">
<p><a href="#_ftnref26" name="_ftn26">[26]</a> Ibid.</p>
</div>
<div id="ftn27">
<p><a href="#_ftnref27" name="_ftn27">[27]</a> RAND CORP, supra note 16, at pg 39.</p>
</div>
<div id="ftn28">
<p><a href="#_ftnref28" name="_ftn28">[28]</a> Ibid.</p>
</div>
<div id="ftn29">
<p><a href="#_ftnref29" name="_ftn29">[29]</a> RAND CORP, supra note 16, at pg 41.</p>
</div>
<div id="ftn30">
<p><a href="#_ftnref30" name="_ftn30">[30]</a> Data-Smart City Solutions. "Dr. George Mohler: Mathematician and Crime Fighter." Data-Smart City Solutions, May 8, 2013. http://datasmart.ash.harvard.edu/news/article/dr.-george-mohler-mathematician-and-crime-fighter-166.</p>
</div>
<div id="ftn31">
<p><a href="#_ftnref31" name="_ftn31">[31]</a> RAND CORP, supra note 16, at pg 44.</p>
</div>
<div id="ftn32">
<p><a href="#_ftnref32" name="_ftn32">[32]</a> Joh, supra note 1, at pg 45.</p>
</div>
<div id="ftn33">
<p><a href="#_ftnref33" name="_ftn33">[33]</a> Ouellette, Danielle. "Dispatch - A Hot Spots Experiment: Sacramento Police Department," June 2012. http://cops.usdoj.gov/html/dispatch/06-2012/hot-spots-and-sacramento-pd.asp.</p>
</div>
<div id="ftn34">
<p><a href="#_ftnref34" name="_ftn34">[34]</a> Pitney Bowes Business Insight. "The Safer Derbyshire Partnership." Derbyshire, 2013. http://www.mapinfo.com/wp-content/uploads/2013/05/safer-derbyshire-casestudy.pdf.</p>
</div>
<div id="ftn35">
<p><a href="#_ftnref35" name="_ftn35">[35]</a> Ibid.</p>
</div>
<div id="ftn36">
<p><a href="#_ftnref36" name="_ftn36">[36]</a> Daniel B Neill, Wilpen L. Gorr. "Detecting and Preventing Emerging Epidemics of Crime," 2007.</p>
</div>
<div id="ftn37">
<p><a href="#_ftnref37" name="_ftn37">[37]</a> RAND CORP, supra note 16, at pg 33.</p>
</div>
<div id="ftn38">
<p><a href="#_ftnref38" name="_ftn38">[38]</a> Joh, supra note 1, at pg 46.</p>
</div>
<div id="ftn39">
<p><a href="#_ftnref39" name="_ftn39">[39]</a> Paul, Jeffery S, and Thomas M. Joiner. "Integration of Centralized Intelligence with Geographic Information Systems: A Countywide Initiative." Geography and Public Safety 3, no. 1 (October 2011): 5-7.</p>
</div>
<div id="ftn40">
<p><a href="#_ftnref40" name="_ftn40">[40]</a> Mohler, supra note 30.</p>
</div>
<div id="ftn41">
<p><a href="#_ftnref41" name="_ftn41">[41]</a> Ibid.</p>
</div>
<div id="ftn42">
<p><a href="#_ftnref42" name="_ftn42">[42]</a> Moses, B., Lyria, & Chan, J. (2014). Using Big Data for Legal and Law Enforcement <br /> Decisions: Testing the New Tools (SSRN Scholarly Paper No. ID 2513564). Rochester, NY: Social Science Research Network. Retrieved from http://papers.ssrn.com/abstract=2513564</p>
</div>
<div id="ftn43">
<p><a href="#_ftnref43" name="_ftn43">[43]</a> Gorner, Jeremy. "Chicago Police Use Heat List as Strategy to Prevent Violence." Chicago Tribune. August 21, 2013. http://articles.chicagotribune.com/2013-08-21/news/ct-met-heat-list-20130821_1_chicago-police-commander-andrew-papachristos-heat-list.</p>
</div>
<div id="ftn44">
<p><a href="#_ftnref44" name="_ftn44">[44]</a> Stroud, Matt. "The Minority Report: Chicago's New Police Computer Predicts Crimes, but Is It Racist?" The Verge. Accessed November 13, 2015. http://www.theverge.com/2014/2/19/5419854/the-minority-report-this-computer-predicts-crime-but-is-it-racist.</p>
</div>
<div id="ftn45">
<p><a href="#_ftnref45" name="_ftn45">[45]</a> Moser, Whet. "The Small Social Networks at the Heart of Chicago Violence." Chicago Magazine, December 9, 2013. http://www.chicagomag.com/city-life/December-2013/The-Small-Social-Networks-at-the-Heart-of-Chicago-Violence/.</p>
</div>
<div id="ftn46">
<p><a href="#_ftnref46" name="_ftn46">[46]</a> Lester, Aaron. "Police Clicking into Crimes Using New Software." Boston Globe, March 18, 2013. https://www.bostonglobe.com/business/2013/03/17/police-intelligence-one-click-away/DzzDbrwdiNkjNMA1159ybM/story.html.</p>
</div>
<div id="ftn47">
<p><a href="#_ftnref47" name="_ftn47">[47]</a> Stanley, Jay. "Chicago Police 'Heat List' Renews Old Fears About Government Flagging and Tagging." American Civil Liberties Union, February 25, 2014. https://www.aclu.org/blog/chicago-police-heat-list-renews-old-fears-about-government-flagging-and-tagging.</p>
</div>
<div id="ftn48">
<p><a href="#_ftnref48" name="_ftn48">[48]</a> Rieke, Aaron, David Robinson, and Harlan Yu. "Civil Rights, Big Data, and Our Algorithmic Future," September 2014. https://bigdata.fairness.io/wp-content/uploads/2015/04/2015-04-20-Civil-Rights-Big-Data-and-Our-Algorithmic-Future-v1.2.pdf.</p>
</div>
<div id="ftn49">
<p><a href="#_ftnref49" name="_ftn49">[49]</a> Edmond, Deepu Sebastian. "Jhakhand's Digital Leap." Indian Express, September 15, 2013. http://www.jhpolice.gov.in/news/jhakhands-digital-leap-indian-express-15092013-18219-1379316969.</p>
</div>
<div id="ftn50">
<p><a href="#_ftnref50" name="_ftn50">[50]</a> Jharkhand Police. "Jharkhand Police IT Vision 2020 - Effective Shared Open E-Governance." 2012. http://jhpolice.gov.in/vision2020. See slide 2</p>
<p><a href="#_ftnref51" name="_ftn51">[51]</a> Edmond, supra note 49.</p>
</div>
<div id="ftn52">
<p><a href="#_ftnref52" name="_ftn52">[52]</a> Edmond, supra note 49.</p>
</div>
<div id="ftn53">
<p><a href="#_ftnref53" name="_ftn53">[53]</a> Kumar, Raj. "Enter, the Future of Policing - Cops to Team up with IIM Analysts to Predict & Prevent Incidents." The Telegraph. August 28, 2012. http://www.telegraphindia.com/1120828/jsp/jharkhand/story_15905662.jsp#.VkXwxvnhDWK.</p>
<p><a href="#_ftnref54" name="_ftn54">[54]</a> Ibid.</p>
</div>
<div id="ftn54"></div>
<div id="ftn55">
<p><a href="#_ftnref55" name="_ftn55">[55]</a> Ibid.</p>
</div>
<div id="ftn56">
<p><a href="#_ftnref56" name="_ftn56">[56]</a> Ibid.</p>
</div>
<div id="ftn57">
<p><a href="#_ftnref57" name="_ftn57">[57]</a> See supra note 49.</p>
</div>
<div id="ftn58">
<p><a href="#_ftnref58" name="_ftn58">[58]</a> See <a href="http://dashboard.jhpolice.gov.in/">here</a> for Jharkhand Police crime dashboard.</p>
</div>
<div id="ftn59">
<p><a href="#_ftnref59" name="_ftn59">[59]</a> Lavanya Gupta, and Selva Priya. "Predicting Crime Rates for Predictive Policing." Gandhian Young Technological Innovation Award, December 29, 2014. http://gyti.techpedia.in/project-detail/predicting-crime-rates-for-predictive-policing/3545.</p>
</div>
<div id="ftn60">
<p><a href="#_ftnref60" name="_ftn60">[60]</a> Gupta, Lavanya. "Minority Report: Minority Report." Accessed November 13, 2015. http://cmuws2014.blogspot.in/2015/01/minority-report.html.</p>
</div>
<div id="ftn61">
<p><a href="#_ftnref61" name="_ftn61">[61]</a> See supra note 59.</p>
</div>
<div id="ftn62">
<p><a href="#_ftnref62" name="_ftn62">[62]</a> See <a href="http://bprd.nic.in/showfile.asp?lid=1224">here</a> for details about 44th All India Police Science Congress.</p>
</div>
<div id="ftn63">
<p><a href="#_ftnref63" name="_ftn63">[63]</a> India, Press Trust of. "Police Science Congress in Gujarat to Have DRDO Exhibition." Business Standard India, March 10, 2015. http://www.business-standard.com/article/pti-stories/police-science-congress-in-gujarat-to-have-drdo-exhibition-115031001310_1.html.</p>
</div>
<div id="ftn64">
<p><a href="#_ftnref64" name="_ftn64">[64]</a> National Crime Records Bureau. "About Crime and Criminal Tracking Network & Systems - CCTNS." Accessed November 13, 2015. http://ncrb.gov.in/cctns.htm.</p>
</div>
<div id="ftn65">
<p><a href="#_ftnref65" name="_ftn65">[65]</a> Ibid. (See index page)</p>
</div>
<div id="ftn66">
<p><a href="#_ftnref66" name="_ftn66">[66]</a> U.S. Const. amend. IV, available <a href="https://www.law.cornell.edu/constitution/fourth_amendment">here</a></p>
</div>
<div id="ftn67">
<p><a href="#_ftnref67" name="_ftn67">[67]</a> United States v Katz, 389 U.S. 347 (1967) , see <a href="https://supreme.justia.com/cases/federal/us/389/347/case.html">here</a></p>
</div>
<div id="ftn68">
<p><a href="#_ftnref68" name="_ftn68">[68]</a> See supra note 1, at pg 60.</p>
</div>
<div id="ftn69">
<p><a href="#_ftnref69" name="_ftn69">[69]</a> See supra note 1, at pg 60.</p>
</div>
<div id="ftn70">
<p><a href="#_ftnref70" name="_ftn70">[70]</a> Villasenor, John. "What You Need to Know about the Third-Party Doctrine." The Atlantic, December 30, 2013. http://www.theatlantic.com/technology/archive/2013/12/what-you-need-to-know-about-the-third-party-doctrine/282721/.</p>
</div>
<div id="ftn71">
<p><a href="#_ftnref71" name="_ftn71">[71]</a> Smith v Maryland, 442 U.S. 735 (1979), see <a href="https://supreme.justia.com/cases/federal/us/442/735/case.html">here</a></p>
</div>
<div id="ftn72">
<p><a href="#_ftnref72" name="_ftn72">[72]</a> United States v Jones, 565 U.S. ___ (2012), see <a href="https://supreme.justia.com/cases/federal/us/565/10-1259/">here</a></p>
</div>
<div id="ftn73">
<p><a href="#_ftnref73" name="_ftn73">[73]</a> Newell, Bryce Clayton. "Local Law Enforcement Jumps on the Big Data Bandwagon: Automated License Plate Recognition Systems, Information Privacy, and Access to Government Information." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, October 16, 2013. http://papers.ssrn.com/abstract=2341182, at pg 24.</p>
</div>
<div id="ftn74">
<p><a href="#_ftnref74" name="_ftn74">[74]</a> See supra note 72.</p>
</div>
<div id="ftn75">
<p><a href="#_ftnref75" name="_ftn75">[75]</a> Dahyabhai Chhaganbhai Thakker vs State Of Gujarat, 1964 AIR 1563</p>
</div>
<div id="ftn76">
<p><a href="#_ftnref76" name="_ftn76">[76]</a> See supra note 16.</p>
</div>
<div id="ftn77">
<p><a href="#_ftnref77" name="_ftn77">[77]</a> See supra note 66.</p>
</div>
<div id="ftn78">
<p><a href="#_ftnref78" name="_ftn78">[78]</a> Brinegar v. United States, 338 U.S. 160 (1949), see <a href="https://supreme.justia.com/cases/federal/us/338/160/case.html">here</a></p>
</div>
<div id="ftn79">
<p><a href="#_ftnref79" name="_ftn79">[79]</a> Terry v. Ohio, 392 U.S. 1 (1968), see <a href="https://supreme.justia.com/cases/federal/us/392/1/case.html">here</a></p>
</div>
<div id="ftn80">
<p><a href="#_ftnref80" name="_ftn80">[80]</a> Ferguson, Andrew Guthrie. "Big Data and Predictive Reasonable Suspicion." SSRN Scholarly Paper. Rochester, NY: Social Science Research Network, April 4, 2014. http://papers.ssrn.com/abstract=2394683, at pg 287. See also supra note 79.</p>
</div>
<div id="ftn81">
<p><a href="#_ftnref81" name="_ftn81">[81]</a> See supra note 80.</p>
</div>
<div id="ftn82">
<p><a href="#_ftnref82" name="_ftn82">[82]</a> See supra note 80.</p>
</div>
<div id="ftn83">
<p><a href="#_ftnref83" name="_ftn83">[83]</a> See supra note 80.</p>
</div>
<div id="ftn84">
<p><a href="#_ftnref84" name="_ftn84">[84]</a> See supra note 80, at pg 289.</p>
</div>
<div id="ftn85">
<p><a href="#_ftnref85" name="_ftn85">[85]</a> Illinois v. Gates, 462 U.S. 213 (1983). See <a href="https://supreme.justia.com/cases/federal/us/462/213/case.html">here</a></p>
</div>
<div id="ftn86">
<p><a href="#_ftnref86" name="_ftn86">[86]</a> See Alabama v. White, 496 U.S. 325 (1990). See <a href="https://supreme.justia.com/cases/federal/us/496/325/">here</a></p>
</div>
<div id="ftn87">
<p><a href="#_ftnref87" name="_ftn87">[87]</a> See supra note 80, at pg 291.</p>
</div>
<div id="ftn88">
<p><a href="#_ftnref88" name="_ftn88">[88]</a> See supra note 80, at pg 293.</p>
</div>
<div id="ftn89">
<p><a href="#_ftnref89" name="_ftn89">[89]</a> See supra note 80, at pg 308.</p>
</div>
<div id="ftn90">
<p><a href="#_ftnref90" name="_ftn90">[90]</a> Ibid.</p>
</div>
<div id="ftn91">
<p><a href="#_ftnref91" name="_ftn91">[91]</a> Ibid.</p>
</div>
<div id="ftn92">
<p><a href="#_ftnref92" name="_ftn92">[92]</a> Larissa Cespedes-Yaffar, Shayona Dhanak, and Amy Stephenson. "U.S. v. Mendenhall, U.S. v. Sokolow, and the Drug Courier Profile Evidence Controversy." Accessed July 6, 2015. http://courses2.cit.cornell.edu/sociallaw/student_projects/drugcourier.html.</p>
</div>
<div id="ftn93">
<p><a href="#_ftnref93" name="_ftn93">[93]</a> Ibid.</p>
</div>
<div id="ftn94">
<p><a href="#_ftnref94" name="_ftn94">[94]</a> United States v. Sokolow, 490 U.S. 1 (1989), see <a href="https://supreme.justia.com/cases/federal/us/490/1/">here</a></p>
</div>
<div id="ftn95">
<p><a href="#_ftnref95" name="_ftn95">[95]</a> See supra note 80, at pg 295.</p>
</div>
<div id="ftn96">
<p><a href="#_ftnref96" name="_ftn96">[96]</a> See supra note 80, at pg 297.</p>
</div>
<div id="ftn97">
<p><a href="#_ftnref97" name="_ftn97">[97]</a> See supra note 80, at pg 308.</p>
</div>
<div id="ftn98">
<p><a href="#_ftnref98" name="_ftn98">[98]</a> See supra note 80, at pg 310.</p>
</div>
<div id="ftn99">
<p><a href="#_ftnref99" name="_ftn99">[99]</a> See supra note 11.</p>
</div>
<div id="ftn100">
<p><a href="#_ftnref100" name="_ftn100">[100]</a> See supra note 11.</p>
</div>
<div id="ftn101">
<p><a href="#_ftnref101" name="_ftn101"><sup><sup>[101]</sup></sup></a> <sup> </sup> See supra note 80, at pg 303.</p>
</div>
<div id="ftn102">
<p><a href="#_ftnref102" name="_ftn102">[102]</a> See supra note 80, at pg 300.</p>
</div>
<div id="ftn103">
<p><a href="#_ftnref103" name="_ftn103">[103]</a> Illinois v. Wardlow, 528 U.S. 119 (2000), see <a href="https://supreme.justia.com/cases/federal/us/528/119/case.html">here</a></p>
</div>
<div id="ftn104">
<p><a href="#_ftnref104" name="_ftn104">[104]</a> Ibid.</p>
</div>
<div id="ftn105">
<p><a href="#_ftnref105" name="_ftn105">[105]</a> See supra note 80, at pg 301.</p>
</div>
<div id="ftn106">
<p><a href="#_ftnref106" name="_ftn106">[106]</a> Ibid.</p>
</div>
<div id="ftn107">
<p><a href="#_ftnref107" name="_ftn107">[107]</a> See supra note 1, at pg 42.</p>
</div>
<div id="ftn108">
<p><a href="#_ftnref108" name="_ftn108">[108]</a> See supra note 80, at pg 303.</p>
</div>
<div id="ftn109">
<p><a href="#_ftnref109" name="_ftn109">[109]</a> See supra note 80, at pg 303.</p>
</div>
<div id="ftn110">
<p><a href="#_ftnref110" name="_ftn110">[110]</a> Ibid.</p>
</div>
<div id="ftn111">
<p><a href="#_ftnref111" name="_ftn111">[111]</a> Ibid.</p>
</div>
<div id="ftn112">
<p><a href="#_ftnref112" name="_ftn112">[112]</a> Ibid.</p>
</div>
<div id="ftn113">
<p><a href="#_ftnref113" name="_ftn113">[113]</a> See supra note 80, at pg 312.</p>
</div>
<div id="ftn114">
<p><a href="#_ftnref114" name="_ftn114">[114]</a> See supra note 80, at pg 317.</p>
</div>
<div id="ftn115">
<p><a href="#_ftnref115" name="_ftn115">[115]</a> See supra note 80, at pg 319.</p>
</div>
<div id="ftn116">
<p><a href="#_ftnref116" name="_ftn116">[116]</a> See supra note 80, at pg 321.</p>
</div>
<div id="ftn117">
<p><a href="#_ftnref117" name="_ftn117">[117]</a> Section 165 Indian Criminal Procedure Code, see <a href="http://indiankanoon.org/doc/996365/">here</a></p>
</div>
<div id="ftn118">
<p><a href="#_ftnref118" name="_ftn118">[118]</a> Gulab Chand Upadhyaya vs State Of U.P, 2002 CriLJ 2907</p>
</div>
<div id="ftn119">
<p><a href="#_ftnref119" name="_ftn119">[119]</a> Section 41 Indian Criminal Procedure Code</p>
</div>
<div id="ftn120">
<p><a href="#_ftnref120" name="_ftn120">[120]</a> See supra note 79</p>
</div>
<div id="ftn121">
<p><a href="#_ftnref121" name="_ftn121">[121]</a> State of Punjab v. Balbir Singh. (1994) 3 SCC 299</p>
</div>
<div id="ftn122">
<p><a href="#_ftnref122" name="_ftn122">[122]</a> Ibid.</p>
</div>
<div id="ftn123">
<p><a href="#_ftnref123" name="_ftn123">[123]</a> Section 41 and 42 in The Narcotic Drugs and Psychotropic Substances Act 1985, see <a href="http://indiankanoon.org/doc/1727139/">here</a></p>
</div>
<div id="ftn124">
<p><a href="#_ftnref124" name="_ftn124">[124]</a> <i>Partap Singh (Dr)</i> v. <i>Director of Enforcement, Foreign Exchange Regulation Act. </i>(1985) 3 SCC 72 : 1985 SCC (Cri) 312 : 1985 SCC (Tax) 352 : AIR 1985 SC 989</p>
</div>
<div id="ftn125">
<p><a href="#_ftnref125" name="_ftn125">[125]</a> Ibid, at SCC pg 77-78.</p>
</div>
<div id="ftn126">
<p><a href="#_ftnref126" name="_ftn126">[126]</a> See supra note 121, at pg 313.</p>
</div>
<div id="ftn127">
<p><a href="#_ftnref127" name="_ftn127">[127]</a> Carlson, Mr David. "Exclusionary Rule." LII / Legal Information Institute, June 10, 2009. https://www.law.cornell.edu/wex/exclusionary_rule.</p>
</div>
<div id="ftn128">
<p><a href="#_ftnref128" name="_ftn128">[128]</a> Ibid.</p>
</div>
<div id="ftn129">
<p><a href="#_ftnref129" name="_ftn129">[129]</a> Mapp v Ohio, 367 U.S. 643 (1961), see <a href="https://supreme.justia.com/cases/federal/us/367/643/case.html">here</a></p>
</div>
<div id="ftn130">
<p><a href="#_ftnref130" name="_ftn130">[130]</a> Ibid.</p>
</div>
<div id="ftn131">
<p><a href="#_ftnref131" name="_ftn131">[131]</a> Busby, John C. "Fruit of the Poisonous Tree." LII / Legal Information Institute, September 21, 2009. https://www.law.cornell.edu/wex/fruit_of_the_poisonous_tree.</p>
</div>
<div id="ftn132">
<p><a href="#_ftnref132" name="_ftn132">[132]</a> Silverthorne Lumber Co., Inc. v. United States, 251 U.S. 385 (1920), see <a href="https://supreme.justia.com/cases/federal/us/251/385/case.html">here</a>.</p>
</div>
<div id="ftn133">
<p><a href="#_ftnref133" name="_ftn133">[133]</a> Beck v. Ohio, 379 U.S. 89 (1964), see <a href="https://supreme.justia.com/cases/federal/us/379/89/case.html">here</a>.</p>
</div>
<div id="ftn134">
<p><a href="#_ftnref134" name="_ftn134">[134]</a> State of Maharashtra v. Natwarlal Damodardas Soni, (1980) 4 SCC 669, at 673.</p>
</div>
<div id="ftn135">
<p><a href="#_ftnref135" name="_ftn135">[135]</a> Ibid.</p>
</div>
<div id="ftn136">
<p><a href="#_ftnref136" name="_ftn136">[136]</a> Radhakishan v. State of U.P. [AIR 1963 SC 822 : 1963 Supp 1 SCR 408, 411, 412 : (1963) 1 Cri LJ 809]</p>
</div>
<div id="ftn137">
<p><a href="#_ftnref137" name="_ftn137">[137]</a> Ibid, at SCR pg 411-12.</p>
</div>
<div id="ftn138">
<p><a href="#_ftnref138" name="_ftn138">[138]</a> <i>Shyam Lal Sharma</i> v. <i>State of M.P</i>. (1972) 1 SCC 764 : 1974 SCC (Cri) 470 : AIR 1972 SC 886</p>
</div>
<div id="ftn139">
<p><a href="#_ftnref139" name="_ftn139">[139]</a> See supra note 135, at page 674.</p>
</div>
<div id="ftn140">
<p><a href="#_ftnref140" name="_ftn140">[140]</a> See supra note 119, at para. 10.</p>
</div>
<div id="ftn141">
<p><a href="#_ftnref141" name="_ftn141">[141]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn142">
<p><a href="#_ftnref142" name="_ftn142">[142]</a> State of Punjab v. Wassan Singh, (1981) 2 SCC 1 : 1981 SCC (Cri) 292</p>
</div>
<div id="ftn143">
<p><a href="#_ftnref143" name="_ftn143">[143]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn144">
<p><a href="#_ftnref144" name="_ftn144">[144]</a> Sunder Singh v. State of U.P, AIR 1956 SC 411 : 1956 Cri LJ 801</p>
</div>
<div id="ftn145">
<p><a href="#_ftnref145" name="_ftn145">[145]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn146">
<p><a href="#_ftnref146" name="_ftn146">[146]</a> Matajog Dobey v.H.C. Bhari, AIR 1956 SC 44 : (1955) 2 SCR 925 : 1956 Cri LJ 140</p>
</div>
<div id="ftn147">
<p><a href="#_ftnref147" name="_ftn147">[147]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn148">
<p><a href="#_ftnref148" name="_ftn148">[148]</a> R v. Sang, (1979) 2 All ER 1222, 1230-31</p>
</div>
<div id="ftn149">
<p><a href="#_ftnref149" name="_ftn149">[149]</a> See supra note 121, at pg 309.</p>
</div>
<div id="ftn150">
<p><a href="#_ftnref150" name="_ftn150">[150]</a> Ibid.</p>
</div>
<div id="ftn151">
<p><a href="#_ftnref151" name="_ftn151">[151]</a> Ibid.</p>
</div>
<div id="ftn152">
<p><a href="#_ftnref152" name="_ftn152">[152]</a> Harlow, Carol, and Richard Rawlings. <i>Law and Administration</i>. 3rd ed. Law in Context. Cambridge University Press, 2009.</p>
</div>
<div id="ftn153">
<p><a href="#_ftnref153" name="_ftn153">[153]</a> <i>R.M. Malkani v. State of Maharashtra,</i> (1973) 1 SCC 471</p>
</div>
<div id="ftn154">
<p><a href="#_ftnref154" name="_ftn154">[154]</a> Kuruma, Son of Kanju v. R., (1955) AC 197</p>
</div>
<div id="ftn155">
<p><a href="#_ftnref155" name="_ftn155">[155]</a> See supra note 154, at 477.</p>
</div>
<div id="ftn156">
<p><a href="#_ftnref156" name="_ftn156">[156]</a> Indian Const. Art 15, see <a href="http://indiankanoon.org/doc/609295/">here</a></p>
</div>
<div id="ftn157">
<p><a href="#_ftnref157" name="_ftn157">[157]</a> Ibid.</p>
</div>
<div id="ftn158">
<p><a href="#_ftnref158" name="_ftn158">[158]</a> See supra note 11.</p>
</div>
</div>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/predictive-policing-what-is-it-how-it-works-and-it-legal-implications'>http://editors.cis-india.org/internet-governance/blog/predictive-policing-what-is-it-how-it-works-and-it-legal-implications</a>
</p>
No publisherRohan GeorgeInternet GovernanceBig DataPrivacy2015-11-24T16:31:41ZBlog Entry