The Centre for Internet and Society
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Vidhi Doshi - Fingerprint Payments Prompt Privacy Fears in India (The Guardian)
http://editors.cis-india.org/internet-governance/news/vidhi-doshi-fingerprint-payments-prompt-privacy-fears-in-india-the-guardian
<b>This article by Vidhi Doshi on the use of Aadhaar-based payments by private companies in India was published by The Guardian on February 09, 2017. Sumandro Chattapadhyay is quoted in the article.</b>
<p>Originally published by <a href="https://www.theguardian.com/sustainable-business/2017/feb/09/fingerprint-payments-privacy-fears-india-banknotes">The Guardian</a>.</p>
<hr />
<p style="text-align: justify;">For two years, Indian officials have been trawling the country, from city slums to unelectrified villages, zapping eyeballs, scanning fingerprints and taking photographs.</p>
<p style="text-align: justify;">Last month, Indian shoppers started to see the results. With the launch of a government-backed fingerprint payment system, tied to India’s growing biometric data bank, registered citizens can – in theory at least – now pay for things with the touch of a finger.</p>
<p style="text-align: justify;">India’s extraordinary biometric database, named Aadhaar after a Hindi word for ‘foundation’, is the biggest of its kind in the world. It was initially sold to the public as a welfare delivery mechanism that would ensure the country’s 1.25bn citizens were each receiving the right quantity of subsidised rice or cooking fuel, while weeding out fraudsters.</p>
<p>But now this pool of more than a billion people’s biometric data is being used by banks, credit checking firms and other private companies to identify customers, raising questions about privacy and security.</p>
<p style="text-align: justify;">As one of his flagship policies, prime minister Narendra Modi pledged to create a “digital India” in which the country’s cash-centric economy would switch to credit and debit cards, squeezing the parallel economy of untaxed cash transactions and giving more citizens access to digital financial services.</p>
<p style="text-align: justify;">In a surprise television announcement last November, Modi announced the demonetisation of 500 and 1,000 rupee notes (around £6 and £12), wiping out 85% of the country’s circulating currency overnight.</p>
<p style="text-align: justify;">Two days later, when the banks reopened, long queues snaked around almost every branch, with millions lining up to open bank accounts for the first time. Many used their 12-digit Aadhaar number, linked to their biometric profile, to sign up. Within three weeks, 3m bank accounts had been opened using fingerprint verification, according to estimates.</p>
<p style="text-align: justify;">The moment marked a radical change for India’s banking system, under which applicants were traditionally required to file photocopies of passports or voter IDs. Banks could take weeks, sometimes months, to verify them. Now applicants’ encrypted biometric data can be sent to the Unique Identification Authority of India (UIDAI), a government agency, to be matched against their Aadhaar data, re-encrypted and sent back to the bank.</p>
<p style="text-align: justify;">Despite technical teething problems, the system is designed to allow very fast authorisation. “All this happens in a matter or two or three seconds,” explains Ajay Bhushan Pandey, UIDAI’s director general.</p>
<p style="text-align: justify;">For Pandey, the benefits are clear: paper documents are easy to forge and hard to verify, especially in India where until recently thousands of people still used handwritten passports. Not so biometric data.</p>
<h4>Privacy fears</h4>
<p style="text-align: justify;">Pandey emphasises that private banks and companies aren’t able to access the entire Aadhaar database, only to use the government interface, which allows them to verify identities.</p>
<p style="text-align: justify;">Nonetheless, many Indians are worried about the privacy implications. Sumandro Chattapadhyay, a director at the Centre for Internet and Society thinktank, is one of them.</p>
<p style="text-align: justify;">For starters, says Chattapadhyay, the law governing use of the biometric database, fast-tracked through parliament last year, is flimsy when it comes to the private sector. Since India lacks a general privacy or data protection law, this leaves corporate use of Aadhaar services effectively unregulated, he says.</p>
<p style="text-align: justify;">This is particularly worrying, says Chattapadhyay, because of the data-sharing possibilities opened up by Aadhaar. It makes it easier for companies not only to share information on individuals’ consumption and mobility habits, but also to link this data up with public records like the electoral register, he says. “Both lead to significant threats to privacy of individuals.”</p>
<p style="text-align: justify;">Chattapadhyay’s fear is that private companies could eventually gain access to government-held personal data, such as income or medical records, while the government could use company data like phone records to target specific individuals in political campaigns.</p>
<p style="text-align: justify;">Already companies are linking Aadhaar numbers with collected metadata. Credit-checking startup CreditVidya, for example, identifies clients using their biometric ID in combination with their internet browsing history and other data, to assign credit scores for users who have no record of loan repayments. Banks then store this processed metadata, for example whether or not someone’s Facebook name is consistent with the name on their bank account.</p>
<p style="text-align: justify;">Its founder Abhishek Agarwal admits there are risks for users: “[I]f someone managed to hack the bank’s security system, as well as the Aadhaar database, they could potentially be able to link your Facebook or LinkedIn data with your biometric information.” But he says this would be hard to do.</p>
<p style="text-align: justify;">Pandey insists the companies are carefully vetted before they can use Aadhaar authentication. But, like Agarwal, he acknowledges the system can never be 100% secure: ““I wouldn’t say it is impossible to break the system, but it is very, very difficult.”</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/vidhi-doshi-fingerprint-payments-prompt-privacy-fears-in-india-the-guardian'>http://editors.cis-india.org/internet-governance/news/vidhi-doshi-fingerprint-payments-prompt-privacy-fears-in-india-the-guardian</a>
</p>
No publisherVidhi DoshiDemonetisationDigital PaymentBig DataPrivacyInternet GovernanceAadhaarBiometrics2017-02-13T09:21:42ZBlog EntrySeminar on Understanding Financial Technology, Cashless India, and Forced Digitalisation (Delhi, January 24)
http://editors.cis-india.org/internet-governance/news/seminar-on-understanding-financial-technology-cashless-india-and-forced-digitalisation-delhi-jan-24-2017
<b>The Centre for Financial Accountability is organising a seminar on "Understanding Financial Technology, Cashless India, and Forced Digitalisation" on Tuesday, January 24, at YWCA, Ashoka Road, New Delhi. Sumandro Chattapadhyay will participate in the seminar and speak on the emerging architecture of FinTech in India, as being developed and deployed by UIDAI and NPCI.</b>
<p> </p>
<p><em>Cross-posted from <a href="https://letstalkfinancialaccountability.wordpress.com/2017/01/20/understanding-financial-technology-cashless-india-forced-digitalisation/">Centre for Financial Accountability</a>.</em></p>
<hr />
<h2>Programme Schedule</h2>
<h4>09.30 - Registration</h4>
<h4>10:00 - Introduction to the Seminar & Setting the Context</h4>
<p>Madhuresh Kumar, National Alliance of People’s Movements</p>
<h4>10:15–11:30 - Session 1 - Understanding the Political Context of FinTech</h4>
<p>B P Mathur, Former Dy CAG</p>
<p>Prabir Purkayastha, Free Software Movement of India and Knowledge Commons</p>
<p>C P Chandrasekhar, Centre for Economic Studies and Planning, JNU</p>
<h4>11:30-11:45 – Tea / Coffee break</h4>
<h4>11:45-13:15 - Session 2 - How will FinTech Impact the Poor, and Labour and Banking Sector?</h4>
<p>Ashim Roy, New Trade Union of India</p>
<p>Nikhil Dey, Mazdoor Kisan Shakti Sangathan</p>
<p>Ravinder Gupta, General Secretary, State Bank of India Officers Association</p>
<h4>13:15-14:00 – Lunch</h4>
<h4>14:00-15:30 - Session 3 - Understanding the Economic Context of FinTech</h4>
<p>Indira Rajaraman, Former Director, RBI</p>
<p>Tony Joseph, Sr. Journalist</p>
<h4>15:30-17:00 - Session 4 - Understanding the Architecture of FinTech: Linkages to Aadhaar, IndiaStack etc</h4>
<p>Sumandro Chattapadhyay, the Centre for Internet and Society</p>
<p>Gopal Krishna, ToxicsWatch</p>
<h4>17:00 – Tea</h4>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/seminar-on-understanding-financial-technology-cashless-india-and-forced-digitalisation-delhi-jan-24-2017'>http://editors.cis-india.org/internet-governance/news/seminar-on-understanding-financial-technology-cashless-india-and-forced-digitalisation-delhi-jan-24-2017</a>
</p>
No publishersumandroUnified Payments InterfaceFinancial TechnologyDigital IDBig DataDigital EconomyUIDInternet GovernanceDigital IndiaAadhaarFinancial InclusionBiometricsDigital Payment2017-01-23T13:17:19ZBlog EntryComments on the Report of the Committee on Digital Payments (December 2016)
http://editors.cis-india.org/internet-governance/blog/comments-on-the-report-of-the-committee-on-digital-payments-dec-2016
<b>The Committee on Digital Payments constituted by the Ministry of Finance and chaired by Ratan P. Watal, Principal Advisor, NITI Aayog, submitted its report on the "Medium Term Recommendations to Strengthen Digital Payments Ecosystem" on December 09, 2016. The report was made public on December 27, and comments were sought from the general public. Here are the comments submitted by the Centre for Internet and Society.</b>
<p> </p>
<h3><strong>1. Preliminary</strong></h3>
<p><strong>1.1.</strong> This submission presents comments by the Centre for Internet and Society (“CIS”) <strong>[1]</strong> in response to the report of the Committee on Digital Payments, chaired by Mr. Ratan P. Watal, Principal Advisor, NITI Aayog, and constituted by the Ministry of Finance, Government of India (“the report”) <strong>[2]</strong>.</p>
<h3><strong>2. The Centre for Internet and Society</strong></h3>
<p><strong>2.1.</strong> The Centre for Internet and Society, CIS, is a non-profit organisation that undertakes interdisciplinary research on internet and digital technologies from policy and academic perspectives. The areas of focus include digital accessibility for persons with diverse abilities, access to knowledge, intellectual property rights, openness (including open data, free and open source software, open standards, and open access), internet governance, telecommunication reform, digital privacy, and cyber-security.</p>
<p><strong>2.2.</strong> CIS is not an expert organisation in the domain of banking in general and payments in particular. Our expertise is in matters of internet and communication governance, data privacy and security, and technology regulation. We deeply appreciate and are most inspired by the Ministry of Finance’s decision to invite entities from both the sectors of finance and information technology. This submission is consistent with CIS’ commitment to safeguarding general public interest, and the interests and rights of various stakeholders involved, especially the citizens and the users. CIS is thankful to the Ministry of Finance for this opportunity to provide a general response on the report.</p>
<h3><strong>3. Comments</strong></h3>
<p><strong>3.1.</strong> CIS observes that the decision by the Government of India to withdraw the legal tender character of the old high denomination banknotes (that is, Rs. 500 Rs. 1,000 notes), declared on November 08, 2016 <strong>[3]</strong>, have generated <strong>unprecedented data about the user base and transaction patterns of digital payments systems in India, when pushed to its extreme use due to the circumstances</strong>. The majority of this data is available with the National Payments Corporation of India and the Reserve Bank of India. CIS requests the authorities concerned to consider <strong>opening up this data for analysis and discussion by public at large and experts in particular, before any specific policy and regulatory decisions are taken</strong> towards advancing digital payments proliferation in India. This is a crucial opportunity for the Ministry of Finance to embrace (open) data-driven regulation and policy-making.</p>
<p><strong>3.2.</strong> While the report makes a reference to the European General Data Protection Directive, it does not make a reference to any substantive provisions in the Directive which may be relevant to digital payments. Aside from the recommendation that privacy protections around the purpose limitation principle be relaxed to ensure that payment service providers be allowed to process data to improve fraud monitoring and anti-money laundering services, the report is silent on significant privacy and data protection concerns posed by digital payments services. <strong>CIS strongly warns that the existing data protection and security regulations under Information Technology (Reasonable security practices and procedures and sensitive personal data or information), Rules are woefully inadequate in their scope and application to effectively deal with potential privacy concerns posed by digital payments applications and services.</strong> Some key privacy issues that must be addressed either under a comprehensive data protection legislation or a sector specific financial regulation are listed below. The process of obtaining consent must be specific, informed and unambiguous and through a clear affirmative action by the data subject based upon a genuine choice provided along with an option to opt out at any stage. The data subjects should have clear and easily enforceable right to access and correct their data. Further, data subjects should have the right to restrict the usage of their data in circumstances such as inaccuracy of data, unlawful purpose and data no longer required in order to fulfill the original purpose.</p>
<p><strong>3.3.</strong> The initial recommendation of the report is to “[m]ake regulation of payments independent from the function of central banking” (page 22). This involves a fundamental transformation of the payment and settlement system in India and its regulation. <strong>We submit that a decision regarding transformation of such scale and implications is taken after a more comprehensive policy discussion, especially involving a wider range of stakeholders</strong>. The report itself notes that “[d]igital payments also have the potential of becoming a gateway to other financial services such as credit facilities for small businesses and low-income households” (page 32). Thus, a clear functional, and hence regulatory, separation between the (digital) payments industry and the lending/borrowing industry may be either effective or desirable. Global experience tells us that digital transactions data, along with other alternative data, are fast becoming the basis of provision of financial and other services, by both banking and non-banking (payments) companies. We appeal to the Ministry of Finance to adopt a comprehensive and concerted approach to regulating, enabling competition, and upholding consumers’ rights in the banking sector at large.</p>
<p><strong>3.4.</strong> The report recognises “banking as an activity is separate from payments, which is more of a technology business” (page 154). Contemporary banking and payment businesses are both are primarily technology businesses where information technology particularly is deployed intimately to extract, process, and drive asset management decisions using financial transaction data. Further, with payment businesses (such as, pre-paid instruments) offering return on deposited money via other means (such as, cashbacks), and potentially competing and/or collaborating with established banks to use financial transaction data to drive lending decisions, including but not limited to micro-loans, it appears unproductive to create a separation between banking as an activity and payments as an activity merely in terms of the respective technology intensity of these sectors. <strong>CIS firmly recommends that regulation of these financial services and activities be undertaken in a technology-agnostic manner, and similar regulatory regimes be deployed on those entities offering similar services irrespective of their technology intensity or choice</strong>.</p>
<p><strong>3.5.</strong> The report highlights two major shortcomings of the current regulatory regime for payments. Firstly “the law does not impose any obligation on the regulator to promote competition and innovation in the payments market” (page 153). It appears to us that the regulator’s role should not be to promote market expansion and innovation but to ensure and oversee competition. <strong>We believe that the current regulator should focus on regulating the existing market, and the work of the expansion of the digital payments market in particular and the digital financial services market in general be carried out by another government agency, as it creates conflict of interest for the regulator otherwise.</strong> Secondly, the report mentions that Payment and Settlement Systems Act does not “focus the regulatory attention on the need for consumer protection in digital payments” and then it notes that a “provision was inserted to protect funds collected from customers” in 2015 (page 153). <strong>This indicates that the regulator already has the responsibility to ensure consumer protection in digital payments. The purview and modalities of how this function of course needs discussion and changes with the growth in digital payments</strong>.</p>
<p><strong>3.6.</strong> The report identifies the high cost of cash as a key reason for the government’s policy push towards digital payments. Further, it mentions that a “sample survey conducted in 2014 across urban and rural neighbourhoods in Delhi and Meerut, shows that despite being keenly aware of the costs associated with transacting in cash, most consumers see three main benefits of cash, viz. freedom of negotiations, faster settlements, and ensuring exact payments” (page 30). It further notes that “[d]igital payments have significant dependencies upon power and telecommunications infrastructure. Therefore, the roll out of robust and user friendly digital payments solutions to unelectrified areas/areas without telecommunications network coverage, remains a challenge.” <strong>CIS much appreciates the discussion of the barriers to universal adoption and rollout of digital payments in the report, and appeals to the Ministry of Finance to undertake a more comprehensive study of the key investments required by the Government of India to ensure that digital payments become ubiquitously viable as well as satisfy the demands of a vast range of consumers that India has</strong>. The estimates about investment required to create a robust digital payment infrastructure, cited in the report, provide a great basis for undertaking studies such as these.</p>
<p><strong>3.7.</strong> CIS is very encouraged to see the report highlighting that “[w]ith the rising number of users of digital payment services, it is absolutely necessary to develop consumer confidence on digital payments. Therefore, it is essential to have legislative safeguards to protect such consumers in-built into the primary law.” <strong>We second this recommendation and would like to add further that financial transaction data is governed under a common data protection and privacy regime, without making any differences between data collected by banking and non-banking entities</strong>.</p>
<p><strong>3.8.</strong> We are, however, very discouraged to see the overtly incorrect use of the word “Open Access” in this report in the context of a payment system disallowing service when the client wants to transact money with a specific entity <strong>[4]</strong>. This is not an uncommon anti-competitive measure adopted by various platform players and services providers so as to disallow users from using competing products (such as, not allowing competing apps in the app store controlled by one software company). <strong>The term “Open Access” is not only the appropriate word to describe the negation of such anti-competitive behaviour, its usage in this context undermines its accepted meaning and creates confusion regarding the recommendation being proposed by the report.</strong> The closest analogy to the recommendation of the report would perhaps be with the principle of “network neutrality” that stands for the network provider not discriminating between data packets being processed by them, either in terms of price or speed.</p>
<p><strong>3.9.</strong> A major recommendation by the report involves creation of “a fund from savings generated from cash-less transactions … by the Central Government,” which will use “the trinity of JAM (Jan Dhan, Adhaar, Mobile) [to] link financial inclusion with social protection, contributing to improved Social and Financial Security and Inclusion of vulnerable groups/ communities” (page 160-161). <strong>This amounts to making Aadhaar a mandatory ID for financial inclusion of citizens, especially the marginal and vulnerable ones, and is in direct contradiction to the government’s statements regarding the optional nature of the Aadhaar ID, as well as the orders by the Supreme Court on this topic</strong>.</p>
<p><strong>3.10.</strong> The report recommends that “Aadhaar should be made the primary identification for KYC with the option of using other IDs for people who have not yet obtained Aadhaar” (page 163) and further that “Aadhaar eKYC and eSign should be a replacement for paper based, costly, and shared central KYC registries” (page 162). <strong>Not only these measures would imply making Aadhaar a mandatory ID for undertaking any legal activity in the country, they assume that the UIDAI has verified and audited the personal documents submitted by Aadhaar number holders during enrollment.</strong> A mandate for <em>replacement</em> of the paper-based central KYC agencies will only remove a much needed redundancy in the the identity verification infrastructure of the government.</p>
<p><strong>3.11.</strong> The report suggests that “[t]ransactions which are permitted in cash without KYC should also be permitted on prepaid wallets without KYC” (page 164-165). This seems to negate the reality that physical verification of a person remains one of the most authoritative identity verification process for a natural person, apart from DNA testing perhaps. <strong>Thus, establishing full equivalency of procedure between a presence-less transaction and one involving a physically present person making the payment will only amount to removal of relatively greater security precautions for the former, and will lead to possibilities of fraud</strong>.</p>
<p><strong>3.12.</strong> In continuation with the previous point, the report recommends promotion of “Aadhaar based KYC where PAN has not been obtained” and making of “quoting Aadhaar compulsory in income tax return for natural persons” (page 163). Both these measures imply a replacement of the PAN by Aadhaar in the long term, and a sharp reduction in growth of new PAN holders in the short term. <strong>We appeal for this recommendation to be reconsidered as integration of all functionally separate national critical information infrastructures (such as PAN and Aadhaar) into a single unified and centralised system (such as Aadhaar) engenders massive national and personal security threats</strong>.</p>
<p><strong>3.13.</strong> The report suggest the establishment of “a ranking and reward framework” to recognise and encourage for the best performing state/district/agency in the proliferation of digital payments. <strong>It appears to us that creation of such a framework will only lead to making of an environment of competition among these entities concerned, which apart from its benefits may also have its costs. For example, the incentivisation of quick rollout of digital payment avenues by state government and various government agencies may lead to implementation without sufficient planning, coordination with stakeholders, and precautions regarding data security and privacy</strong>. The provision of central support for digital payments should be carried out in an environment of cooperation and not competition.</p>
<p><strong>3.14.</strong> CIS welcomes the recommendation by the report to generate greater awareness about cost of cash, including by ensuring that “large merchants including government agencies should account and disclose the cost of cash collection and cash payments incurred by them periodically” (page 164). It, however, is not clear to whom such periodic disclosures should be made. <strong>We would like to add here that the awareness building must simultaneously focus on making public how different entities shoulder these costs. Further, for reasons of comparison and evidence-driven policy making, it is necessary that data for equivalent variables are also made open for digital payments - the total and disaggregate cost, and what proportion of these costs are shouldered by which entities</strong>.</p>
<p><strong>3.15.</strong> The report acknowledges that “[t]oday, most merchants do not accept digital payments” and it goes on to recommend “that the Government should seize the initiative and require all government agencies and merchants where contracts are awarded by the government to provide at-least one suitable digital payment option to its consumers and vendors” (page 165). This requirement for offering digital payment option will only introduce an additional economic barrier for merchants bidding for government contracts. <strong>We appeal to the Ministry of Finance to reconsider this approach of raising the costs of non-digital payments to incentivise proliferation of digital payments, and instead lower the existing economic and other barriers to digital payments that keep the merchants away</strong>. The adoption of digital payments must not lead to increasing costs for merchants and end-users, but must decrease the same instead.</p>
<p><strong>3.16.</strong> As the report was submitted on December 09, 2016, and was made public only on December 27, 2016, <strong>it would have been much appreciated if at least a month-long window was provided to study and comment on the report, instead of fifteen days</strong>. This is especially crucial as the recently implemented demonetisation and the subsequent banking and fiscal policy decisions taken by the government have rapidly transformed the state and dynamics of the payments system landscape in India in general, and digital payments in particular.</p>
<h3><strong>Endnotes</strong></h3>
<p><strong>[1]</strong> See: <a href="http://cis-india.org/">http://cis-india.org/</a>.</p>
<p><strong>[2]</strong> See: <a href="http://finmin.nic.in/reports/Note-watal-report.pdf">http://finmin.nic.in/reports/Note-watal-report.pdf</a> and <a href="http://finmin.nic.in/reports/watal_report271216.pdf">http://finmin.nic.in/reports/watal_report271216.pdf</a>.</p>
<p><strong>[3]</strong> See: <a href="http://finmin.nic.in/cancellation_high_denomination_notes.pdf">http://finmin.nic.in/cancellation_high_denomination_notes.pdf</a>.</p>
<p><strong>[4]</strong> Open Access refers to “free and unrestricted online availability” of scientific and non-scientific literature. See: <a href="http://www.budapestopenaccessinitiative.org/read">http://www.budapestopenaccessinitiative.org/read</a>.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/comments-on-the-report-of-the-committee-on-digital-payments-dec-2016'>http://editors.cis-india.org/internet-governance/blog/comments-on-the-report-of-the-committee-on-digital-payments-dec-2016</a>
</p>
No publisherSumandro Chattapadhyay and Amber SinhaUIDDigital IDBig DataDigital EconomyDigital AccessPrivacyDigital SecurityData RevolutionDigital PaymentInternet GovernanceDigital IndiaData ProtectionDemonetisationHomepageFeaturedAadhaar2017-01-12T12:32:22ZBlog EntryNew Media, personalisation and the role of algorithms
http://editors.cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms
<b>In his much acclaimed book, The Filter Bubble, Eli Pariser explains how personalisation of services on the web works and laments that they are creating individual bubbles for each user, which run counter to the idea of the Internet as an inherently open place. While Pariser’s book looks at the practices of various large companies providing online services, he briefly touches upon the role of new media such as search engines and social media portals in new curation. Building upon Pariser’s unexplored argument, this article looks at the impact of algorithmic decision-making and Big Data in the context of news reporting and curation.</b>
<em><br /></em>
<blockquote>
<div>
<div><em>Everything which bars freedom and fullness of communication sets up barriers that divide human beings into sets and cliques, into antagonistic sects and factions, and thereby undermines the democratic way of life. </em>—John Dewey</div>
</div>
</blockquote>
<p> Eli Pariser, in his book, The Filter Bubble,[1] refers to the scholarship by Walter Lippmann and John Dewey as integral to the evolution of the understanding of the democratic and ethical duties of the Fourth Estate. Lippmann was disillusioned by the role of newspapers in propaganda for the First World War. He responded with three books in quick succession — Liberty and the News,[2] Public Opinion[3] and The Phantom Public.[4] Lippmann brought attention the fact that the process of news-reporting was conducted through privately determined and unexamined standards. The failure of the Fourth Estate to perform its democratic functions, was, in the opinion of Lippmann, one of the prime factors responsible for the public not being an informed and rational entity. John Dewey, while rejecting Lippmann’s arguments that matters of public policy can only be determined by inside experts with training and education, did acknowledge the his critique of the media.</p>
<p>Pariser points to the creation of a wall between editorial decisionmaking and advertiser interests, as the eventual result of the Lippmann and Dewey debate. While accepting that this division between the financial and reporting sides of media houses has not been always observed, Pariser emphasises that the fact that the standard exists is important.[5] Unlike traditional media, the new media which relies on algorithmic decision-making for personalisation is not subject to the same standards which try to mitigate the influence of commercial interests on editorial decisions while performing many of the same functions as the traditional media.[6] </p>
<h3>How personalisation algorithms work</h3>
<p dir="ltr">Kevin Slavin, at his famous talk in the TEDGLobal Conference, characterised algorithms as “maths that computers use to decide stuff” and that it was infiltrating every aspect of our lives.[7] According to Slavin’s view, algorithms can be seen as control technologies and shape our world constantly through media and information systems, dynamically modifying content and function through these programmed routines. Search engines and social media platforms perpetually rank user-generated content through algorithms.[8]</p>
<p>Personalisation technologies have various advantages. It translates into more relevant content, which for service providers means more clicks and revenue and for consumer, less time spent on finding the content.[9] However, it also leads to privacy compromise, lack of control and reduced individual capability.[10] Search engines like Google use the famous PageRank algorithm, which combined with geographical location and previous searches yields most relevant search results.[11] PageRank algorithm uses various real time variables dependent on both voluntary and involuntary user inputs. These variables include number of clicks, number of occurrences of the key terms and number of references by other credible pages etc. This data in turn determines the order of pages in search results and influences the way we perceive, understand and analyse information.[12] Maps showing real time traffic information retrieve data from laser and infrared sensors alongside the road and from information from devices of users. Once this real time data is combined with historical trends, these maps recommend rout to every user, hence influencing the traffic patterns.[13]</p>
<p>Even though this phenomenon of personalization may appears to be new, it has been prevalent in the society for ages.[14] The history of mass media culture clearly shows personalization has always been a method to increase market, market reach and customer satisfaction.[15] Newspapers have sections dedicated to special topics, radio and TV have channels dedicated to different interest groups, age groups and consumers.[16] These personalised sections in a newspaper and personalised channels on radio and television don’t just provide greater satisfaction to the readers or listeners or consumers, they also provide targeted advertisement space for the advertisers and content developers. However, digital footprints and mass collection of data have made this phenomenon much more granular and detailed. Geographical location of an individual can tell a lot about their community, their culture and other important traits local to a community.[17] This data further assists in personalisation. Current developments in technology not only help in better collection of data about personal preferences but also help in better personalisation.</p>
<p>Pariser mentions three ways in which the personalization technologies of this day are different from those of the past. First, for the very first time, individuals are alone in the filter bubble. While in traditional forms of personalisation, there were various individuals who shared the same frame of reference, now there is a separate sets of filters governing the dissemination of content to each individual.[18] Second, the personalisation technologies are entirely invisible now, and there is little that consumers can do to control or modify them.[19] Third, often the decision to be subject to these personalisation technologies is not an informed choice. A good example of this would be an individual’s geographical location.[20]</p>
<h3>The neutrality of New Media?</h3>
<p dir="ltr">More and more, we have noticed personalisation technologies having an impact on how we consume news on the Internet. Google News, Facebook’s News Feed which tries to put together a dynamic feed for both personal and global stories, and Twitter’s trending hashtag feature, have brought forward these services are key drivers of an emerging news ecosystem. Initially, this new media was hailed as a natural consequence of the Internet which would enable greater public participation, allow journalists to find more stories and engage with the readers directly. An illustration of the same could be seen in the way Internet based news media and social networking websites behaved in the aftermath of Israel’s attacks on a United Nations run school in Gaza strip. While much of the international Internet media covered the story, Israel’s home media did not cover the story. The only exception to this was the liberal Israeli news website Ha’aretz.[21] Network graph details of Twitter, for a few days immediately after the incident clearly show the social media manifestation of the event in the personalised cyberspace. It is clearly visible that when most of the word was re-tweeting news of this heinous act of Israel, Israeli’s hardly re-tweeted this news. In fact they were busty re-tweeting the news of rocket attacks on Israel.[22]</p>
<p>The use of social media in newsmaking was hailed by many scholars as symptomatic of the decentralisation characteristic of the Internet. It has been seen as movement towards greater grassroots participation by negating the ‘gatekeeping’ role traditionally played by editors. Thomas Poell and José van Dijck punch holes in theory of social media and other online technologies as mere facilitators of user participation and translators of user preferences through Big Data analytics.[23] They quote T. Gillespie’s work which talks of the narrative of these online services as platforms which are “open, neutral, egalitarian and progressive support for activity.”[24]</p>
<p>Pedro Domingos calls the overwhelming number of choices as the defining problem of the information age, and machine learning and data analytics as the largest part of this solution.[25] The primary function of algorithmic decision making in the context of consumption of content is to narrow down the choices. Domingos is more optimistic about the impact of these technologies, and he says “last step of the decision is usually still for humans to make, but learners intelligently reduce the choices to something a human can manage.”[26] On the other hand, Pariser is more circumspect about the coercive result of machine learning algorithms. Whichever way we lean, we have to accept that a large part of personalisation algorithms is to select and prioritize content by categorising it on the basis of relevance and popularity. </p>
<p>Poell and van Dijck call this a new knowledge logic which in effect replaces human judgement (as, earlier exercised by editors) to some kind of proxy decisionmaking based on data. Their main thesis is that there is little evidence to suggest that the latter is more democratic than former and creates new problems of its own. They go on to compare the practices of various services including Facebook’s new graph and Twitter’s trending topic, and conclude that they prioritise breaking news stories over other kinds of content.[27] For instance, the algorithm for the trending topics depends not on the volume but the velocity of the tweets with the hashtag or term. It could be argued that given this predilection, the algorithms will rarely prefer complex content. If we go by Lippmann and Dewey’s idea that the role of the Fourth Estate is to inform public debate and accountability of those in positions of power, this aspect of Big Data algorithms does not correspond with this role.</p>
<h3>Quantified Audience</h3>
<p dir="ltr">Another aspect of use of Big Data and algorithms in New Media that requires attention is that the networked infrastructure enables a quantified audience. C W Anderson who has studied newsroom practices in the US looked at role played by audience quantification and rationalization in shifting newswork practices. He concluded that more and more, journalists are less autonomous in their news decisions and increasingly reliant on audience metrics as a supplement to news judgment.[28] Poell and van Dijck review the the practices by some leading publications such a New York Times, L.A. Times and Huffington Post, and degree to which audience metrics dictates editorial decisions. While New York Times seems to prioritise content on their social media portals based on expectation of spike in user traffic, L.A. Times goes one step further by developing content specifically aimed towards promoting greater social participation. Neither of these practices though compare to the reliance on SEO and SMO strategies of web-born news providers like Huffington Post. They have traffic editors who trawl the Internet for trending topics and popular search terms, the feedback from them dictates the content creation.[29]</p>
<h3>Conclusion</h3>
<p dir="ltr">The above factors demonstrate that the idea of New Media leading to the Fourth Estate performing its democratic functions does not take into account the actual practices. This idea is based on the erroneous assumption that technology, in general and algorithms, in particular are neutral. While the emergence of New Media might have reduced the gatekeeping role played by the editors, its strong prioritisation of content that will be popular reduce the validity of arguments that it leads to more informed public discussion. As Pariser said, the traditional media scores over the New Media inasmuch as there is an existence of a standard of division between editorial decisionmaking and advertiser interest. While this standard is flouted by media houses all the time, it exists as a metric to aspire to and measure service providers against. The New Media performs many of the same functions and maybe it is time to evolve some principles and ethical standards that take into account the need for it to perform these democratic functions.</p>
<h3>Endnotes </h3>
<p class="normal"><sup><sup>[1]</sup></sup> Eli Pariser, The Filter Bubble: What the Internet is
hiding from you (The Penguin Press, New York, 2011) </p>
<p dir="ltr"><span class="MsoFootnoteReference"><span class="MsoFootnoteReference">[2]</span></span> Walter Lippmann, Liberty and News (Harcourt, Brace
and Howe, New York 1920) available at<a href="https://archive.org/details/libertyandnews01lippgoog">https://archive.org/details/libertyandnews01lippgoog</a></p>
<p class="normal"><sup><sup>[3]</sup></sup> Walter Lippmann, Public Opinion (Harcourt, Brace and
Howe, New York 1920) available at <a href="http://xroads.virginia.edu/~Hyper2/CDFinal/Lippman/cover.html">http://xroads.virginia.edu/~Hyper2/CDFinal/Lippman/cover.html</a></p>
<p class="normal"><sup><sup>[4]</sup></sup> Walter Lippmann, The Phantom Public (Transaction
Publishers, New York, 1925)</p>
<p class="normal"><sup><sup>[5]</sup></sup> <em>Supra</em> Note
1 at 35.</p>
<p class="normal"><sup><sup>[6]</sup></sup> <em>Supra</em> Note
1 at 36.</p>
<p class="normal"><sup><sup>[7]</sup></sup> <a href="https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world/transcript?language=en">https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world/transcript?language=en</a></p>
<p class="normal"><sup><sup>[8]</sup></sup> Fenwick McKelvey, “Algorithmic Media Need Democratic
Methods: Why Publics Matter”, available at <a href="http://www.fenwickmckelvey.com/wp-content/uploads/2014/11/2746-9231-1-PB.pdf">http://www.fenwickmckelvey.com/wp-content/uploads/2014/11/2746-9231-1-PB.pdf</a>.</p>
<p class="normal"><sup><sup>[9]</sup></sup> <a href="http://mashable.com/2011/06/03/filters-eli-pariser/#9tIHrpa_9Eq1">http://mashable.com/2011/06/03/filters-eli-pariser/#9tIHrpa_9Eq1</a></p>
<p class="normal"><sup><sup>[10]</sup></sup> Helen Ashman, Tim Brailsford, Alexandra Cristea, Quan
Z Sheng, Craig Stewart, Elaine Torns and Vincent Wade, “The ethical and social
implications of personalization technologies for e-learning” available at <a href="http://www.sciencedirect.com/science/article/pii/S0378720614000524">http://www.sciencedirect.com/science/article/pii/S0378720614000524</a>.</p>
<p class="normal"><sup><sup>[11]</sup></sup> Sergey Brin and Lawrence Page, “The Anatomy of a
Large-Scale Hypertextual Web Search Engine” available at <a href="http://infolab.stanford.edu/pub/papers/google.pdf">http://infolab.stanford.edu/pub/papers/google.pdf</a>.</p>
<p class="normal"><sup><sup>[12]</sup></sup> Ian Rogers, “The Google Pagerank Algorithm and How It
Works” available at <a href="http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm">http://www.cs.princeton.edu/~chazelle/courses/BIB/pagerank.htm</a>.</p>
<p class="normal"><sup><sup>[13]</sup></sup> Trygve Olson and Terry Nelson, “The Internet’s Impact
on Political Parties and Campaigns”, available at <a href="http://www.kas.de/wf/doc/kas_19706-544-2-30.pdf?100526130942">http://www.kas.de/wf/doc/kas_19706-544-2-30.pdf?100526130942</a>.</p>
<p class="normal"><sup><sup>[14]</sup></sup> Ian Witten, “Bias, privacy and and personalisation on
the web”, available at <a href="http://www.cs.waikato.ac.nz/~ihw/papers/07-IHW-Bias,privacyonweb.pdf">http://www.cs.waikato.ac.nz/~ihw/papers/07-IHW-Bias,privacyonweb.pdf</a>.</p>
<p class="normal"><sup><sup>[15]</sup></sup> <em>Supra</em> Note
1 at 10.</p>
<p class="normal"><sup><sup>[16]</sup></sup> <a href="https://www.americanpressinstitute.org/publications/reports/survey-research/social-demographic-differences-news-habits-attitudes/">https://www.americanpressinstitute.org/publications/reports/survey-research/social-demographic-differences-news-habits-attitudes/</a></p>
<p class="normal"><sup><sup>[17]</sup></sup> Charles Heatwole, “Culture: A Geographical Perspective”
available at <a href="http://www.p12.nysed.gov/ciai/socst/grade3/geograph.html">http://www.p12.nysed.gov/ciai/socst/grade3/geograph.html</a>.</p>
<p class="normal"><sup><sup>[18]</sup></sup> <em>Supra</em> Note
1 at 10.</p>
<p class="normal"><sup><sup>[19]</sup></sup> <em>Id</em>.</p>
<p class="normal"><sup><sup>[20]</sup></sup> <em>Supra</em> Note
1 at 11.</p>
<p class="normal"><sup><sup>[21]</sup></sup> Paul Mason, “Why Israel is losing the social media
war over Gaza?” available at <a href="http://blogs.channel4.com/paul-mason-blog/impact-social-media-israelgaza-conflict/1182">http://blogs.channel4.com/paul-mason-blog/impact-social-media-israelgaza-conflict/1182</a>.</p>
<p class="normal"><sup><sup>[22]</sup></sup> Gilad Lotan, Israel, Gaza, War & Data: Social
Networks and the Art of Personalizing Propaganda available at <a href="http://www.huffingtonpost.com/entry/israel-gaza-war-social-networks-data_b_5658557.html">www.huffingtonpost.com/entry/israel-gaza-war-social-networks-data_b_5658557.html</a></p>
<p class="normal"><sup><sup>[23]</sup></sup> Thomas Poell and José van Dijck, “Social Media and
Journalistic Independence” in Media Independence: Working with Freedom or
Working for Free?, edited by James Bennett & Niki Strange. (Routledge,
London, 2015)</p>
<p class="normal"><sup><sup>[24]</sup></sup> T Gillespie, “The politics of ‘platforms,” in New
Media & Society (Volume 12, Issue 3).</p>
<p class="normal"><sup><sup>[25]</sup></sup> Pedro Domingos, The Master Algorithm: How the quest
for the ultimate learning machine will re-make the world (Basic Books, New
York, 2015) at 38.</p>
<p class="normal"><sup><sup>[26]</sup></sup> <em>Ibid</em> at 40.</p>
<p class="normal"><sup><sup>[27]</sup></sup> <em>Supra</em> Note
23.</p>
<p class="normal"><sup><sup>[28]</sup></sup> C W Anderson, Between creative and quantified
audiences: Web metrics and changing patterns of newswork in local US newsrooms,
available at <a href="https://www.academia.edu/10937194/Between_Creative_And_Quantified_Audiences_Web_Metrics_and_Changing_Patterns_of_Newswork_in_Local_U.S._Newsrooms">https://www.academia.edu/10937194/Between_Creative_And_Quantified_Audiences_Web_Metrics_and_Changing_Patterns_of_Newswork_in_Local_U.S._Newsrooms</a></p>
<p dir="ltr">
<sup><sup>[29]</sup></sup> <em>Supra </em>Note 23.</p>
<p dir="ltr"><span id="docs-internal-guid-24b4db2a-a606-d425-16ff-1d76b980367d"><br /></span></p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms'>http://editors.cis-india.org/internet-governance/new-media-personalisation-and-the-role-of-algorithms</a>
</p>
No publisheramberHuman RightsBig DataInternet GovernanceMachine LearningAlgorithmsNew Media2017-01-16T07:20:52ZBlog EntryWorkshop Report - UIDAI and Welfare Services: Exclusion and Countermeasures
http://editors.cis-india.org/internet-governance/blog/workshop-report-uidai-and-welfare-services-august-27-2016
<b>This report presents summarised notes from a workshop organised by the Centre for Internet and Society (CIS) on Saturday, August 27, 2016, to discuss, raise awareness of, and devise countermeasures to exclusion due to implementation of UID-based verification for and distribution of welfare services.</b>
<p> </p>
<h2>Introduction</h2>
<p>The Centre for Internet and Society organised a workshop on "UIDAI and Welfare Services: Exclusion and Countermeasures" at the Institution of Agricultural on Technologists on August 27 in Bangalore to discuss, raise awareness of, and devise countermeasures to exclusion due to implementation of UID-based verification for and distribution of welfare services <strong>[1]</strong>. This was a follow-up to the workshop held in Delhi on “Understanding Aadhaar and its New Challenges” at the Centre for Studies in Science Policy, JNU on May 26th and 27th 2016 <strong>[2]</strong>. In this report we summarise the key concerns raised and the case studies presented by the participants at the workshop held on August 27, 2016.</p>
<h2>Implementation of the UID Project</h2>
<p><strong>Question of Consent:</strong> The Aadhaar Act <strong>[3]</strong> states that the consent of the individual must be taken at the time of enrollment and authentication and it must be informed to him/her the purpose for which the data would be used. However, the Act does not provide for an opt-out mechanism and an individual is compelled to give consent to continue with the enrollment process or to complete an authentication.</p>
<p><strong>Lack of Adherence to Court Orders:</strong> Despite of several orders by Supreme Court stating that use of Aadhaar cannot be made mandatory for the purpose of availing benefits and services, multiple state governments and departments have made it mandatory for a wide range of purposes like booking railway tickets <strong>[4]</strong>, linking below the poverty line ration cards with Aadhaar <strong>[5]</strong>, school examinations <strong>[6]</strong>, food security, pension and scholarship <strong>[7]</strong>, to name a few.</p>
<p><strong>Misleading Advertisements:</strong> A concern was raised that individuals are being mislead in the necessity and purpose for enrollment into the project. For example, people have been asked to enrol by telling them that they might get excluded from the system and cannot get services like passports, banks, NREGA, salaries for government employees, denial of vaccinations, etc. Furthermore, the Supreme Court has ordered Aadhaar not be mandatory, yet people are being told that documentation or record keeping cannot be done without UID number.</p>
<p><strong>Hybrid Governance:</strong> The participants pointed out that with the Aadhaar (Targeted delivery of financial and other subsidies, benefits and services) Act, 2016 (hereinafter referred to as Aadhaar Act, 2016 ) being partially enforced, multiple examples of exclusion as reported in the news are demonstrating how the Aadhaar project is creating a case of hybrid governance i.e private corporations playing a significant role in Governance. This can be seen in case of Aadhaar where we see many entities from private sector being involved in its implementation, as well as many software and hardware companies.</p>
<p><strong>Lack of Transparency around Sharing of Biometric Data:</strong> The fact how and why the Government is relying on biometrics for welfare schemes is unclear and not known. Also, there is no information on how biometric data that is collected through the project is being used and its ability as an authenticating device. Along with that, there is very little information on companies that have been enlisted to hold and manage data and perform authentication.</p>
<p><strong>Possibility of Surveillance:</strong> Multiple petitions and ongoing cases have raised concerns regarding the possibility of surveillance, tracking, profiling, convergence of data, and the opaque involvement of private companies involved in the project.</p>
<p><strong>Denial of Information:</strong> In an RTI filed by one of the participant requesting to share the key contract for the project, it was refused on the grounds under section 8(1) (d) of the RTI Act, 2005. However, it was claimed that the provision would not be applicable since the contract was already awarded and any information disclosed to the Parliament should be disclosed to the citizens. The Central Information Commission issued a letter stating that the contractual obligation is over and a copy of the said agreement can be duly shared. However, it was discovered by the said participant that certain pages of the same were missing , which contained confidential information. When this issue went before appeal before the Information Commissioner, the IC gave an order to the IC in Delhi to comply with the previous order. However, it was communicated that limited financial information may be given, but not missing pages. Also, it was revealed that the UIDAI was supposed to share biometric data with NPR (by way of a MoU), but it has refused to give information since the intention was to discontinue NPR and wanted only UIDAI to collect data.</p>
<h2>Concerns Arising from the Report of the Comptroller and Auditor General of India (CAG) on Implementation of PAHAL (DBTL) Scheme</h2>
<p>A presentation on the CAG compliance audit report of PAHAL on LPG <strong>[8]</strong> revealed how the society was made to believe that UID will help deal with the issue of duplication and collection as well as use of biometric data will help. The report also revealed that multiple LPG connections have the same Aadhaar number or same bank account number in the consumer database maintained by the OMCs, the bank account number of consumers were also not accurately recorded, scrutiny of the database revealed improper capture of Aadhaar numbers, and there was incorrect seeding of IFSC codes in consumer database. The participants felt that this was an example of how schemes that are being introduced for social welfare do not necessarily benefit the society, and on the contrary, has led to exclusion by design. For example, in the year 2011, by was of the The Liquefied Petroleum Gas (Regulation of Supply and Distribution) Amendment Order, 2011 <strong>[9]</strong>, the Ministry of Petroleum and Natural Gas made the Unique Identification Number (UID) under the Aadhaar project a must for availing LPG refills. This received a lot of public pushback, which led to non-implementation of the order. In October 2012, despite the UIDAI stating that the number was voluntary, a number of services began requiring the provision of an Aadhaar number for accessing benefits. In September 2013, when the first order on Aadhaar was passed by court <strong>[10]</strong>, oil marketing companies and UIDAI approached the Supreme Court to change the same and allow them to make it mandatory, which was refused by the Court. Later in the year 2014, use of Aadhaar for subsidies was made mandatory. The participants further criticised the CAG report for revealing the manner in which linking Aadhaar with welfare schemes has allowed duplication and led to ghost beneficiaries where there is no information about who these people are who are receiving the benefits of the subsidies. For example, in Rajasthan, people are being denied their pension as they are being declared dead due to absence of information from the Aadhaar database.</p>
<p>It was said that the statistics of duplication mentioned in the report show how UIDAI (as it claims to ensure de-duplication of beneficiaries) is not required for this purpose and can be done without Aadhaar as well. Also, due to incorrect seeding of Aadhaar number many are being denied subsidy where there is no information regarding the number of people who have been denied the subsidy because of this. Considering these important facts from the audit report, the discussants concluded how the statistics reflect inflated claims by UIDAI and how the problems which are said to be addressed by using Aadhaar can be dealt without it. In this context, it is important to understand how the data in the aadhaar database maybe wrong and in case of e-governance the citizens suffer. Also, the fact that loss of subsidy-not in cash, but in use of LPG cylinder - only for cooking, is ignored. In addition to that, there is no data or way to check if the cylinder is being used for commercial purposes or not as RTI from oil companies says that no ghost identities have been detected.</p>
<h2>UID-linked Welfare Delivery in Rajasthan</h2>
<p>One speaker presented findings on people's experiences with UID-linked welfare services in Rajasthan, collected through a 100 days trip organised to speak to people across the state on problems related to welfare governance. This visit revealed that people who need the benefits and access to subsidies most are often excluded from actual services. It was highlighted that the paperless system is proving to be highly dangerous. Some of the cases discussed included that of a disabled labourer, who was asked to get an aadhaar card, but during enrollment asked the person standing next to him to put all his 5 fingers for biometric data collection. Due to this incorrect data, he is devoid of all subsidies since the authentication fails every time he goes to avail it. He stopped receiving his entitlements. Though problems were anticipated, the misery of the people revealed the extent of the problems arising from the project. In another case, an elderly woman living alone, since she could not go for Aadhaar authentication, had not been receiving the ration she is entitled to receive for the past 8 months. When the ration shop was approached to represent her case, the dealers said that they cannot provide her ration since they would require her thumb print for authentication. Later, they found out that on persuading the dealer to provide her with ration since Aadhaar is not mandatory, they found out that in their records they had actually mentioned that she was being given the ration, which was not the case. So the lack of awareness and the fact that people are entitled to receive the benefits irrespective of Aadhaar is something that is being misused by dealers. This shows how this system has become a barrier for the people, where they are also unaware about the grievance redressal mechanism.</p>
<h2>Aadhaar and e-KYC</h2>
<p>In this session, the use of Aadhaar for e-KYC verification was discussed The UID strategy document describes how the idea is to link UIDAI with money enabled Direct Benefit Transfer (DBT) to the beneficiaries without any reason or justification for the same. It was highlighted by one of the participants how the Reserve Bank of India (RBI) believed that making Aadhaar compulsory for e-KYC and several other banking services was a violation of the Money Laundering Act as well as its own rules and standards, however, later relaxed the rules to link Aadhaar with bank accounts and accepted its for e-KyC with great reluctance as the Department of Revenue thought otherwise. It was mentioned how allowing opening of bank accounts remotely using Aadhaar, without physically being present, was touted as a dangerous idea. However, the restrictions placed by RBI were suddenly done away with and opening bank accounts remotely was enabled via e-KYC.</p>
<p>A speaker emphasised that with emerging FinTech services in India being tied with Aadhaar via India Stack, the following concerns are becoming critical:</p>
<ol><li>With RBI enabling creation of bank accounts remotely, it becomes difficult to to track who did e-KYC and which bank did it and hold the same accountable.<br /><br /></li>
<li>The Aadhaar Act 2016 states that UIDAI will not track the queries made and will only keep a record of Yes/No for authentication. For example, the e-KYC to open a bank account can now be done with the help of an Aadhaar number and biometric authentication. However, this request does not get recorded and at the time of authentication, an individual is simply told whether the request has been matched or not by way of a Yes/No <strong>[11]</strong>. Though UIDAI will maintain the authentication record, this may act as an obstacle since in case the information from the aadhaar database does not match, the person would not be able to open a bank account and would only receive a yes/no as a response to the request.<br /><br /></li>
<li>Further, there is a concern that the Aadhaar Enabled Payment System being implemented by the National Payment Corporation of India (NCPI) would allow effectively hiding of source and destination of money flow, leading to money laundering and cases of bribery. This possible as NCPI maintains a mapper where each bank account is linked (only the latest one). However, Aadhaar number can be linked with multiple bank accounts of an individual. So when a transaction is made, the mapper records the transaction only from that 1 account. But if another transaction takes place with another bank account, that record is not maintained by the mapper at NCPI since it records only transactions of the latest account seeded in that. This makes money laundering easy as the money moves from aadhaar number to aadhaar number now rather than bank account to bank account.</li></ol>
<h2>Endnotes</h2>
<p><strong>[1]</strong> See: <a href="http://cis-india.org/internet-governance/events/uidai-and-welfare-services-exclusion-and-countermeasures-aug-27">http://cis-india.org/internet-governance/events/uidai-and-welfare-services-exclusion-and-countermeasures-aug-27</a>.</p>
<p><strong>[2]</strong> See: <a href="http://cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges">http://cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges</a>.</p>
<p><strong>[3]</strong> See: <a href="https://uidai.gov.in/beta/images/the_aadhaar_act_2016.pdf">https://uidai.gov.in/beta/images/the_aadhaar_act_2016.pdf</a>.</p>
<p><strong>[4]</strong> See: <a href="http://scroll.in/latest/816343/aadhaar-numbers-may-soon-be-compulsory-to-book-railway-tickets">http://scroll.in/latest/816343/aadhaar-numbers-may-soon-be-compulsory-to-book-railway-tickets</a>.</p>
<p><strong>[5]</strong> See: <a href="http://www.thehindu.com/news/national/karnataka/linking-bpl-ration-card-with-aadhaar-made-mandatory/article9094935.ece">http://www.thehindu.com/news/national/karnataka/linking-bpl-ration-card-with-aadhaar-made-mandatory/article9094935.ece</a>.</p>
<p><strong>[6]</strong> See: <a href="http://timesofindia.indiatimes.com/india/After-scam-Bihar-to-link-exams-to-Aadhaar/articleshow/54000108.cms">http://timesofindia.indiatimes.com/india/After-scam-Bihar-to-link-exams-to-Aadhaar/articleshow/54000108.cms</a>.</p>
<p><strong>[7]</strong> See: <a href="http://www.dailypioneer.com/state-editions/cs-calls-for-early-steps-to-link-aadhaar-to-ac.html">http://www.dailypioneer.com/state-editions/cs-calls-for-early-steps-to-link-aadhaar-to-ac.html</a>.</p>
<p><strong>[8]</strong> See: <a href="http://www.cag.gov.in/sites/default/files/audit_report_files/Union_Commercial_Compliance_Full_Report_25_2016_English.pdf">http://www.cag.gov.in/sites/default/files/audit_report_files/Union_Commercial_Compliance_Full_Report_25_2016_English.pdf</a>.</p>
<p><strong>[9]</strong> See: <a href="http://petroleum.nic.in/docs/lpg/LPG%20Control%20Order%20GSR%20718%20dated%2026.09.2011.pdf">http://petroleum.nic.in/docs/lpg/LPG%20Control%20Order%20GSR%20718%20dated%2026.09.2011.pdf</a>.</p>
<p><strong>[10]</strong> See: <a href="http://judis.nic.in/temp/494201232392013p.txt">http://judis.nic.in/temp/494201232392013p.txt</a>.</p>
<p><strong>[11]</strong> Section 8(4) of the Aadhaar Act, 2016 states that "The Authority shall respond to an authentication query with a positive, negative or any other appropriate response sharing such identity information excluding any core biometric information."</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/workshop-report-uidai-and-welfare-services-august-27-2016'>http://editors.cis-india.org/internet-governance/blog/workshop-report-uidai-and-welfare-services-august-27-2016</a>
</p>
No publishervanyaDigital PaymentData SystemsResearchers at WorkUIDInternet GovernanceSurveillanceBig DataAadhaarWelfare GovernanceBig Data for DevelopmentDigital ID2019-03-16T04:34:11ZBlog EntryCFI-ACCION - Panel Discussion on 'Big Data: Challenge or Opportunity?' (Delhi, December 06)
http://editors.cis-india.org/internet-governance/news/cfi-accion-panel-discussion-on-big-data-delhi-dec-06
<b>The Centre for Financial Inclusion of ACCION International is organising a panel discussion on "Big Data: Challenge or Opportunity?" as an associated event of the Inclusive Finance India Summit 2016, Hotel Ashok, Delhi, December 05-06. The discussion will be held at 12:30 on Tuesday, December 06. It will be moderated by Amy Jensen Mowl, CFI Fellow at IFMR, and M.S. Sriram, Distinguished Fellow at the Institute for Development of Research in Banking Technology. Sumandro Chattapadhyay will participate as a panelist.</b>
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<h4>Inclusive Finance India Summit: <a href="http://inclusivefinanceindia.org/">http://inclusivefinanceindia.org/</a>.</h4>
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<img src="https://github.com/cis-india/website/raw/master/img/CFI-ACCION_Discussion-Poster_20161206.jpg" />
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For more details visit <a href='http://editors.cis-india.org/internet-governance/news/cfi-accion-panel-discussion-on-big-data-delhi-dec-06'>http://editors.cis-india.org/internet-governance/news/cfi-accion-panel-discussion-on-big-data-delhi-dec-06</a>
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No publishersumandroFinancial TechnologyBig DataData SystemsBig Data for DevelopmentFinancial InclusionResearchers at Work2019-03-16T04:41:52ZBlog EntryThe Technology behind Big Data
http://editors.cis-india.org/internet-governance/blog/technology-behind-big-data
<b>The authors undertakes a high-level literature review of the most commonly used technological tools and processes in the big data life cycle. The big data life cycle is a conceptual construct that can be used to study the various stages that typically occur in collecting, storing and analysing big data, along with the principles that can govern these processes.</b>
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<h4><a class="external-link" href="http://cis-india.org/internet-governance/files/technology-behind-big-data.pdf/view">Download the Paper</a> (PDF, 277 kb)</h4>
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<h2 style="text-align: justify;">Introduction</h2>
<p style="text-align: justify;">Defining big data is a disputed area in the field of computer science<a name="_ftnref1" href="#_ftn1"><sup><sup>[1]</sup></sup></a>, there is some consensus on a basic structure to its definition<a name="_ftnref2" href="#_ftn2"><sup><sup>[2]</sup></sup></a>. Big data is data that is collected in the form of datasets that has three main criteria: size, variety & velocity, all of which operate at an immense scale<a name="_ftnref3" href="#_ftn3"><sup><sup>[3]</sup></sup></a>. It is ‘big’ in size, often running into petabytes of information, has vast variety within its components, and is created, captured and analysed at an incredibly rapid velocity. All of this also makes big data difficult to handle using traditional technological tools and techniques.</p>
<p style="text-align: justify;">This paper will attempt to perform a high-level literature review of the most commonly used technological tools and processes in the big data life cycle. The big data life cycle is a conceptual construct that can be used to study the various stages that typically occur in collecting, storing and analysing big data, along with the principles that can govern these processes. The big data life cycle consists of four components, which will also be the key structural points of the paper, namely: Data Acquisition, Data Awareness, Data Analytics & Data Governance.<strong><sup>4</sup> </strong>The paper will focus on the aspects that the author believes are relevant for analysing the technological impact of big data on both technology itself and society at large.</p>
<p style="text-align: justify;"><strong>Scope: </strong>The scope of the paper is to study the technology used in big data using the "Life Cycle of Big Data" as model structure to categorise & study the vast range of technologies that are involved in big data. However, the paper will be limited to the study of technology related directly to the big data life cycle. It shall specifically exclude the use/utilisation of big data from its scope since big data is most often being fed into other, unrelated technologies for consumption leading to rather limitless possibilities.</p>
<p style="text-align: justify;"><strong>Goal:</strong> Goal of the paper is twofold: a.) to use the available literature on the technological aspects of big data, to perform a brief overview of the technology in the field and b.) to frame the relevant research questions for studying the technology of big data and its possible impact on society.</p>
<h2 style="text-align: justify;">Data Acquisition</h2>
<p style="text-align: justify;">Acquiring big data has two main sub components to it, the first being sensing the existence of the data’ itself and the second, the stage of collecting and storing this data. Both of these subcomponents are incredibly diverse fields, with lots of rapid change occurring in the technology utilised to carry out these tasks. The section will provide a brief overview of the subcomponents and then discuss the technology used to fulfil the tasks.</p>
<h2 style="text-align: justify;">Data Sensing</h2>
<p style="text-align: justify;">Data does not exist in a vacuum and is always created as a part of a larger process, especially in the aspect of modern technology. Therefore, the source of the data itself plays a vital role in determining how it can be captured and analysed in the larger scheme of things. Entities constantly emit information into the environment that can be utilised for the purposes of big data, leading to two main kinds of data: data that is “born digital” or “born analogue.”<a name="_ftnref4" href="#_ftn4"><sup><sup>[4]</sup></sup></a></p>
<h3 style="text-align: justify;">Born Digital Data</h3>
<p style="text-align: justify;">Information that is “born digital,” is created, by a user or by a digital system, specifically for use by a computer or data‐processing system. This is a vast range of information and newer fields are being added to this category on a daily basis. It includes, as a short, indicative list: email and text messaging, any form of digital input, including keyboards, mouse interactions and touch screens, GPS location data, data from daily home appliances (Internet of Things), etc. All of this data can be tracked and tagged to users as well as be aggregated to form a larger picture, massively increasing the scope of what may constitute the ‘data’ in big data.</p>
<p style="text-align: justify;">Some indicative uses of how such born digital data is catalogued by technological solutions on the user side, prior to being sent for collection/storage are:</p>
<p style="text-align: justify;">a.) Cookies - There are small, often just text, files that are left on user devices by websites in order to that visit, task or action (for example, logging into an email account) with a subsequent event.<a name="_ftnref5" href="#_ftn5"><sup><sup>[5]</sup></sup></a> (for example, revisiting the website)</p>
<p style="text-align: justify;">b.) Website Analytics<a name="_ftnref6" href="#_ftn6"><sup><sup>[6]</sup></sup></a> - Various services, such as Google Analytics, Piwik, etc., can use JavaScript and other web development languages to record a very detailed, intimate track of a user's actions on a website, including how long a user hovers above a link, the time spent on the website/application and in some cases, even the time spent specific aspects of the page.</p>
<p style="text-align: justify;">c.) GPS<a name="_ftnref7" href="#_ftn7"><sup><sup>[7]</sup></sup></a> - With the almost pervasive usage of smartphones with basic location capabilities, GPS sensors on these devices are used to provide regular, minute driven updates to applications, operating systems and even third parties about the user's location. Modern variations such as A-GPS can be used to provide basic positioning information even without satellite coverage, vastly expanding the indoor capabilities of location collection.</p>
<p style="text-align: justify;">All of these instances of sensing born digital data are common terms, used in daily parlance by billions of people from all over the world, which is a symbolic of just how deeply they have pervaded into our daily lifestyle. Apart from privacy & security concerns this in turn also leads to an exponential increase in the data available to collect for any interested party.</p>
<h3 style="text-align: justify;">Sensor Data</h3>
<p style="text-align: justify;">Information is said to be “analogue” when it contains characteristics of the physical world, such as images, video, heartbeats, etc. Such information becomes electronic when processed by a “sensor,” a device that can record physical phenomena and convert it into digital information. Some examples to better illustrate information that is born analogue but collected via digital means are:</p>
<p style="text-align: justify;">a.) Voice and/or video content on devices - Apart from phone calls and other forms communication, video and voice based interactions have started to regularly be captured to provide enhanced services. These include Google Now<a name="_ftnref8" href="#_ftn8"><sup><sup>[8]</sup></sup></a>, Cortana<a name="_ftnref9" href="#_ftn9"><sup><sup>[9]</sup></sup></a> and other digital assistants as well as voice guided navigation systems in cars, etc.</p>
<p style="text-align: justify;">b.) Personal health data such as heartbeats, blood pressure, respiration, velocity, etc. - This personal, potentially very powerful information is collected by dedicated sensors on devices such as Fitbit<a name="_ftnref10" href="#_ftn10"><sup><sup>[10]</sup></sup></a>, Mi Band<a name="_ftnref11" href="#_ftn11"><sup><sup>[11]</sup></sup></a>, etc. as well as by increasingly sophisticated smartphone applications such as Google Fit that can do so without any special device.</p>
<p style="text-align: justify;">c.) Camera on Home Appliances - Cameras and sensors on devices such as video game consoles (Kinect<a name="_ftnref12" href="#_ftn12"><sup><sup>[12]</sup></sup></a> being a relevant example) can record detailed human interactions, which can be mined for vast amounts of information apart from carrying out the basic interactions with the devices itself.</p>
<p style="text-align: justify;">While not as vast a category as born digital data, the increasingly lower costs of technology and ubiquitous usage of digital, networked devices is leading to information that was traditionally analogue in nature to be captured for use at a rapidly increasing rate.</p>
<h2 style="text-align: justify;">Data Collection & Storage</h2>
<p style="text-align: justify;">Traditional data was normally processed using the Extract, Transform, Load (ETL) methodology, which was used to collect the data from outside sources, modify the data to fit needs, and then upload the data into the data storage system for future use.<a name="_ftnref13" href="#_ftn13"><sup><sup>[13]</sup></sup></a> Technology such as spreadsheets, RDBMS databases, Structured Query Languages (SQL), etc. were all initially used to carry out these tasks, more often than not manually. <a name="_ftnref14" href="#_ftn14"><sup><sup>[14]</sup></sup></a></p>
<p style="text-align: justify;">However, for big data, the methodology traditionally followed is both inefficient and insufficient to meet the demands of modern use. Therefore, the Magnetic, Agile, Deep (MAD) process is used to collect and store data<a name="_ftnref15" href="#_ftn15"><sup><sup>[15]</sup></sup></a><a name="_ftnref16" href="#_ftn16"><sup><sup>[16]</sup></sup></a>. The needs and benefits of such a system are: attracting all the data sources regardless of their quality (magnetic), logical and physical contents of storage systems adapting to the rapid data evolution in big data (agile) and complex algorithmic statistical analysis required of big data on a very short notice<a name="_ftnref17" href="#_ftn17"><sup><sup>[17]</sup></sup></a>. (deep)</p>
<p style="text-align: justify;">The technology used to perform data storage using the MAD process requires vast amount of processing power, which is very difficult to create in a single, physical space/unit for nonstate or research entities, who cannot afford supercomputers. Therefore, most solutions used in big data rely on two major components to store data: distributed systems and Massive Parallel Processing<a name="_ftnref18" href="#_ftn18"><sup><sup>[18]</sup></sup></a> (MPP) that run on non-relational (in-memory) database systems. Database performance and reliability is traditionally gauged using pure performance metrics (FLOPS per second, etc.) as well as the Atomicity, consistency, isolation, durability (ACID) criteria.<a name="_ftnref19" href="#_ftn19"><sup><sup>[19]</sup></sup></a> The most commonly used database systems for big data applications are given below. The specific operational qualities and performance of each of these databases is beyond the scope of this review but the common criteria that makes them well suited for big data storage have been delineated below.</p>
<h3 style="text-align: justify;">Non-relational databases</h3>
<p style="text-align: justify;">Databases traditionally used to be structured entities that operated solely on the ability to correlate information stored in them using explicitly defined relationships. Even prior to the advent of big data, this outlook was turning out to be a limiting factor in how large amounts of stored information could be leveraged, this led to the evolution of non relational database systems. Before going into them in detail, a basic primer on their data transfer protocols will be helpful in understanding their operation.</p>
<p style="text-align: justify;">A protocol is a model that structures instructions in a particular manner so that it can be reproduced from one system to another<a name="_ftnref20" href="#_ftn20"><sup><sup>[20]</sup></sup></a><a name="_ftnref21" href="#_ftn21"><sup><sup>[21]</sup></sup></a>. The protocols which govern technology in the case of big data have gone through many stages of evolution, starting off with simple HTML based systems<a name="_ftnref22" href="#_ftn22"><sup><sup>[22]</sup></sup></a>, which then evolved to XML driven SOAP systems<a name="_ftnref23" href="#_ftn23"><sup><sup>[23]</sup></sup></a>, which led to JavaScript Object Notation, or JSON<a name="_ftnref24" href="#_ftn24"><sup><sup>[24]</sup></sup></a>, the currently used form for in most big database systems. JSON is an open format used to transfer data objects, using human-readable text and is the basis for most of the commonly used non-relational database management systems. Examples of Non-relational databases also known as NoSQL databases, include MongoDB<a name="_ftnref25" href="#_ftn25"><sup><sup>[25]</sup></sup></a>, Couchbase<a name="_ftnref26" href="#_ftn26"><sup><sup>[26]</sup></sup></a>, etc. They were developed for both managing as well as storing unstructured data. They aim for scaling, flexibility, and simplified development. Such databases rather focus on the high-performance scalable data storage, and allow tasks to be written in the application layer instead of databases specific languages, allowing for greater interoperability.<a name="_ftnref27" href="#_ftn27"><sup><sup>[27]</sup></sup></a></p>
<h3 style="text-align: justify;">In-Memory Databases</h3>
<p style="text-align: justify;">In order to overcome performance limitation of traditional database systems, some modern databases now use in-memory databases. These systems manage the data in the RAM memory of the server, thus eliminating storage disk input/output. This allows for almost realtime responses from the database, in comparisons to minutes or hours required on traditional database systems. This improvement in the performance is so massive that, entirely new applications are being developed for using IMDB systems.<a name="_ftnref28" href="#_ftn28"><sup><sup>[28]</sup></sup></a> These IMDB systems are also being used for advanced analytics on big data, especially to increase the access speed to data and increase the scoring rate of analytic models for analysis.<a name="_ftnref29" href="#_ftn29"><sup><sup>[29]</sup></sup></a> Examples of IMDB include VoltDB<a name="_ftnref30" href="#_ftn30"><sup><sup>[30]</sup></sup></a>, NuoDB<a name="_ftnref31" href="#_ftn31"><sup><sup>[31]</sup></sup></a>, SolidDB<a name="_ftnref32" href="#_ftn32"><sup><sup>[32]</sup></sup></a> and Apache Spark<a name="_ftnref33" href="#_ftn33"><sup><sup>[33]</sup></sup></a>.</p>
<h2 style="text-align: justify;">Hybrid Systems</h2>
<p style="text-align: justify;">These are the two major systems used to store data prior to it being processed or analysed in a big data application. However, the divide between data storage and data management is a slim one and most database systems also contain various unique attributes that cater them to specific kinds of analysis. (as can be seen from the IMDB example above) One example of a very commonly used Hybrid system that deals with storage as well as awareness of the data is Apache Hadoop<sup>33</sup>, which is detailed below.</p>
<h2 style="text-align: justify;">Apache Hadoop</h2>
<p style="text-align: justify;">Hadoop consists of two main components: the HDFS for the big data storage, and MapReduce for big data analytics, each of which will be detailed in their respective section.</p>
<ol style="text-align: justify;">
<li>The HDFS<a name="_ftnref34" href="#_ftn34"><sup><sup>[34]</sup></sup></a><a name="_ftnref35" href="#_ftn35"><sup><sup>[35]</sup></sup></a> storage function in Hadoop provides a reliable distributed file system, stored across multiple systems for processing & redundancy reasons. The file system is optimized for large files, as single files are split into blocks and spread across systems known as cluster nodes.<a name="_ftnref36" href="#_ftn36"><sup><sup>[36]</sup></sup></a> Additionally, the data is protected among the nodes by a replication mechanism, which ensures availability even if any node fails. Further, there are two types of nodes: Data Nodes and Name Nodes.<a name="_ftnref37" href="#_ftn37"><sup><sup>[37]</sup></sup></a> Data is stored in the form of file blocks across the multiple Data Nodes while the Name Node acts as an intermediary between the client and the Data Node, where it directs the requesting client to the particular Data Node which contains the requested data.</li></ol>
<p style="text-align: justify;">This operating structure for storing data also has various variations within Hadoop such as HBase for key/value pair type queries (a NoSQL based system), Hive for relational type queries, etc. Hadoop’s redundancy, speed, ability to run on commodity hardware, industry support and rapid pace of development have led to it being almost co-equivalently associated with big data.<a name="_ftnref38" href="#_ftn38"><sup><sup>[38]</sup></sup></a></p>
<h2 style="text-align: justify;">Data Awareness</h2>
<p style="text-align: justify;">Data Awareness, in the context of big data, is the task of creating a scheme of relationships within a set of data, to allow different users of the data to determine a fluid yet valid context and utilise it for their desired tasks.<a name="_ftnref39" href="#_ftn39"><sup><sup>[39]</sup></sup></a> It is a relatively new field, in which most of the work is currently being done on semantic structures to allow data to gain context in an interoperable format, in contrast to the current system where data is given context using unique, model specific constructs.<a name="_ftnref40" href="#_ftn40"><sup><sup>[40]</sup></sup></a> (such as XML Schemes, etc.)</p>
<p style="text-align: justify;">Some of the original work on this field was carried out in the form of utilising the Resource Description Framework (RDF), which was built primarily to allow describing of data in a portable manner, especially being agnostic towards platforms and systems for Semantic Web at the W3C. SPARQL is the language used to implement RDF based designs but both largely remain underutilised in both the public domain as well as big data. Authors such as Kurt</p>
<p style="text-align: justify;">Cagle<a name="_ftnref41" href="#_ftn41"><sup><sup>[41]</sup></sup></a> and Bob DuCharme<a name="_ftnref42" href="#_ftn42"><sup><sup>[42]</sup></sup></a> predict its explosion in the next couple of years. Companies have also started realising the value of interoperable context, with Oracle Spatial<a name="_ftnref43" href="#_ftn43"><sup><sup>[43]</sup></sup></a> and IBM’s DB2<a name="_ftnref44" href="#_ftn44"><sup><sup>[44]</sup></sup></a> already including RDF and SPARQL support in the past 3 years.</p>
<p style="text-align: justify;">While underutilised, the rapid developments taking place in the field will make the impact that data awareness may have on big data as big as Hadoop and maybe even SQL. Some aspects of it are already beginning to be used in Artificial Intelligence, Natural Language Processing, etc. with tremendous scope for development.<a name="_ftnref45" href="#_ftn45"><sup><sup>[45]</sup></sup></a></p>
<h2 style="text-align: justify;">Data Processing & Analytics</h2>
<p style="text-align: justify;">Data Processing largely has three primary goals: a. determines if the data collected is internally consistent; b. make the data meaningful to other systems or users using either metaphors or analogy they can understand; and (what many consider most importantly) provide predictions about future events and behaviours based upon past data and trends.<a name="_ftnref46" href="#_ftn46"><sup><sup>[46]</sup></sup></a></p>
<p style="text-align: justify;">Being a very vast field with rapidly changing technologies governing its operation, this section will largely concentrate on the most commonly used technologies in data analytics.</p>
<p style="text-align: justify;">Data analytics requires four primary conditions to be met in order to carry out effective processing: fast, data loading, fast query processing, efficient utilisation of storage and adaptivity to dynamic workload patterns. The analytical model most commonly associated with meeting this criteria and with big data in general is MapReduce, detailed below. There are other, more niche models and algorithms (such as Project Voldemort<a name="_ftnref47" href="#_ftn47"><sup><sup>[47]</sup></sup></a> used by LinkedIn), which are used in big data but they are beyond the scope of the review, and more information about them can be read at article linked in the previous citation. (Reference architecture and classification of technologies, products and services for big data system)</p>
<h2 style="text-align: justify;">MapReduce</h2>
<p style="text-align: justify;">MapReduce is a generic parallel programming concept, derived from the “Map” and “Reduce” of functional programming languages, which makes it particularly suited for big data operations. It is at the core of Hadoop<a name="_ftnref48" href="#_ftn48"><sup><sup>[48]</sup></sup></a>, and performs the data processing and analytics functions in other big data systems as well.<a name="_ftnref49" href="#_ftn49"><sup><sup>[49]</sup></sup></a> The fundamental premise of MapReduce is scaling out rather than scaling up, i.e., (adding more numerical resources, rather than increasing the power of a single system)<a name="_ftnref50" href="#_ftn50"><sup><sup>[50]</sup></sup></a></p>
<p style="text-align: justify;">MapReduce operates by breaking a task down into steps and executing the steps in parallel, across many systems. This comes with two advantages, a reduction in the time needed to finish the task and also a decrease in the amount of resources one has to expend to perform the task, in both power and energy. This model makes it ideally suited for the large data sets and quick response times required of big data operations generally.</p>
<p style="text-align: justify;">The first step of a MapReduce job is to correlate the input values to a set of keys/value pairs as output. The “Map” function then partitions the processing tasks into smaller tasks, and assigns them to the appropriate key/value pairs.<a name="_ftnref51" href="#_ftn51"><sup><sup>[51]</sup></sup></a> This allows unstructured data, such as plain text, to be mapped to a structured key/value pair. As an example, the key could be the punctuation in a sentence and the value of the pair could be the number of occurrences of the punctuation overall. This output of the Map function is then passed on “Reduce” function.<a name="_ftnref52" href="#_ftn52"><sup><sup>[52]</sup></sup></a> Reduce then collects and combines this output, using identical key/value pairs, to provide the final result of the task.<a name="_ftnref53" href="#_ftn53"><sup><sup>[53]</sup></sup></a> These steps are carried using the Job Tracker & Task Tracker in Hadoop but different systems have different methodologies to carry out similar tasks.</p>
<h2 style="text-align: justify;">Data Governance</h2>
<p style="text-align: justify;">Data Governance is the act of managing raw big data as well as the processed information that arises from big data in order to meet legal, regulatory and business imposed requirements. While there is no standardized format for data governance, there have been increasing call with various sectors (especially healthcare) to create such a format to ensure reliable, secure and consistent big data utilisation across the board. The following tactics and techniques have been utilised or suggested for data governance, with varying degrees of success:</p>
<ol style="text-align: justify;">
<li><strong>Zero-knowledge systems</strong>: This technological proposal maintains secrecy with respect to the low-level data while allowing encrypted data to be examined for certain higherlevel abstractions.<a name="_ftnref54" href="#_ftn54"><sup><sup>[54]</sup></sup></a> For the system to be zero-knowledge, the client’s system will have to encrypt the data and send it to the storage provider. Due to this, the provider stores the data in the encrypted format and cannot decipher the same unless he/she is in possession of the key which will decrypt the data into plaintext. This allows the individual to store his data with a storage provider while also maintaining anonymity of the details contained in such information. However, these are currently just beginning to be used in simple situations. As of now, they are not expandable to unstructured and complex cases and have to be developed marginally before they can be used for research and data mining purposes.</li>
<li><strong>Homomorphic encryption</strong>: Homomorphic encryption is a privacy preserving technique which performs searches and other computations over data that is encrypted while also protecting the individual’s privacy.<a name="_ftnref55" href="#_ftn55"><sup><sup>[55]</sup></sup></a> This technique has however been considered to be impractical and is deemed to be an unlikely policy alternative for near future purposes in the context of preserving privacy in the age of big data.<a name="_ftnref56" href="#_ftn56"><sup><sup>[56]</sup></sup></a></li>
<li><strong>Multi-party computation</strong>: In this technique, computation is done on encrypted distributed data stores.<a name="_ftnref57" href="#_ftn57"><sup><sup>[57]</sup></sup></a> This mechanism is closely related to homomorphic encryption where individual data is kept private using encryption algorithms called “collusion-robust” while the same is used to calculate statistics.<a name="_ftnref58" href="#_ftn58"><sup><sup>[58]</sup></sup></a> The parties involved are aware of some private data and each of them use a protocol which produces results based on the information they are aware of and the information they are not aware of, without revealing the data they are not already aware of.<a name="_ftnref59" href="#_ftn59"><sup><sup>[59]</sup></sup></a> Multi-party computations thus help in generating useful data for statistical and research purposes without compromising the privacy of the individuals.</li></ol>
<ol style="text-align: justify;">
<li><strong>Differential Privacy</strong>: Although this technological development is related to encryption, it follows a different technique. Differential privacy aims at maximizing the precision of computations and database queries while reducing the identifiability of the data owners who have records in the database, usually through obfuscation of query results.<a name="_ftnref60" href="#_ftn60"><sup><sup>[60]</sup></sup></a> This is widely applied today in the existence of big data in order to ensure preservation of privacy while trying to reap the benefits of large scale data collection.<a name="_ftnref61" href="#_ftn61"><sup><sup>[61]</sup></sup></a></li>
<li><strong>Searchable encryption</strong>: Through this mechanism, the data subject can make certain data searchable while minimizing exposure and maximizing privacy.<a name="_ftnref62" href="#_ftn62"><sup><sup>[62]</sup></sup></a> The data owner can make his information available through search engines by providing the data in an encrypted format but by adding tags consisting of certain keywords which can be deciphered by the search engine. This encrypted data shows up in the search results when searched with these particular keywords but can only be read when the person is in possession of the key which is required for decrypting the information.</li></ol>
<p style="text-align: justify;">This technique of encryption provides maximum security to the individual’s data and preserves privacy to the greatest possible extent.</p>
<ol style="text-align: justify;">
<li><strong>K-anonymity</strong>: The property of k-anonymity is being applied in the present day in order to preserve privacy and avoid re-identification.<a name="_ftnref63" href="#_ftn63"><sup><sup>[63]</sup></sup></a> A certain data set is said to possess the property of k-anonymity if individual specific data can be released and used for various purposes without re-identification. The analysis of the data should be carried out without attributing the data to the individual to whom it belongs and should give scientific guarantees for the same.</li>
<li><strong>Identity Management Systems</strong>: These systems enable the individuals to establish and safeguard their identities, explain those identities with the help of attributes, follow the activity of their identities and also delete their identities if they wish to.<a name="_ftnref64" href="#_ftn64"><sup><sup>[64]</sup></sup></a> It uses cryptographic schemes and protocols to make anonymous or pseudonymous the identities and credentials of the individuals before analysing the data.</li>
<li><strong>Privacy Preserving Data Publishing</strong>: This is a method in which the analysts are provided with the individual’s personal information with the ability to decipher particular information from the database while preventing the inference of certain other information which might lead to a breach of privacy.<a name="_ftnref65" href="#_ftn65"><sup><sup>[65]</sup></sup></a> Data which is essential for the analysis will be provided for processing while sensitive data will not be disclosed. This tool primarily focuses on microdata.</li>
<li><strong>Privacy Preserving Data Mining</strong>: This mechanism uses perturbation methods and randomization along with cryptography in order to permit data mining on a filtered version of the data which does not contain any form of sensitive information. PPDM focuses on data mining results unlike PPDP.<a name="_ftnref66" href="#_ftn66"><sup><sup>[66]</sup></sup></a> </li></ol>
<h2 style="text-align: justify;">Conclusion</h2>
<p style="text-align: justify;">Studying the technology surrounding big data has led to two major observations: the rapid pace of development in the industry and the stark lack of industry standards or government regulations directed towards big data technologies. These observations have been the primary motivating factor for framing further research in the field. Understanding how to deal with big data technologically, rather than just the potential regulation of possible harms after the technological processes have been performed might be critical for the human rights dialogue as these processes become even more extensive, opaque and technologically complicated.</p>
<hr style="text-align: justify;" />
<p style="text-align: justify;"><a name="_ftn1" href="#_ftnref1">[1]</a> EMC: Data Science and Big Data Analytics. In: EMC Education Services, pp. 1–508 (2012)</p>
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<p style="text-align: justify;"><a name="_ftn4" href="#_ftnref4">[4]</a> Big Data and Privacy: A Technological Perspective - President’s Council of Advisors on Science and</p>
<p style="text-align: justify;">Technology (May 2014)</p>
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<p style="text-align: justify;"><a name="_ftn9" href="#_ftnref9">[9]</a> <em>ibid</em></p>
<p style="text-align: justify;"><a name="_ftn10" href="#_ftnref10">[10]</a> Banaee, Hadi, Mobyen Uddin Ahmed, and Amy Loutfi. "Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges." <em>Sensors</em> 13.12 (2013): 17472-17500.</p>
<p style="text-align: justify;"><a name="_ftn11" href="#_ftnref11">[11]</a> <em>ibid</em></p>
<p style="text-align: justify;"><a name="_ftn12" href="#_ftnref12">[12]</a> Chung, Eric S., John D. Davis, and Jaewon Lee. "Linqits: Big data on little clients." <em>ACM SIGARCH Computer Architecture News</em>. Vol. 41. No. 3. ACM, 2013.</p>
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<p style="text-align: justify;"><a name="_ftn14" href="#_ftnref14">[14]</a> Henry, Scott, et al. "Engineering trade study: extract, transform, load tools for data migration." <em>2005 IEEE Design Symposium, Systems and Information Engineering</em>. IEEE, 2005.</p>
<p style="text-align: justify;"><a name="_ftn15" href="#_ftnref15">[15]</a> Cohen, Jeffrey, et al. "MAD skills: new analysis practices for big data." <em>Proceedings of the VLDB Endowment</em></p>
<p style="text-align: justify;"><a name="_ftn16" href="#_ftnref16">[16]</a> .2 (2009): 1481-1492.</p>
<p style="text-align: justify;"><a name="_ftn17" href="#_ftnref17">[17]</a> Elgendy, Nada, and Ahmed Elragal. "Big data analytics: a literature review paper." <em>Industrial Conference on Data Mining</em>. Springer International Publishing, 2014.</p>
<p style="text-align: justify;"><a name="_ftn18" href="#_ftnref18">[18]</a> Wu, Xindong, et al. "Data mining with big data." <em>IEEE transactions on knowledge and data engineering</em> 26.1 (2014): 97-107.</p>
<p style="text-align: justify;"><a name="_ftn19" href="#_ftnref19">[19]</a> Supra Note 17</p>
<p style="text-align: justify;"><a name="_ftn20" href="#_ftnref20">[20]</a> Hu, Han, et al. "Toward scalable systems for big data analytics: A technology tutorial." <em>IEEE Access</em> 2 (2014):</p>
<p style="text-align: justify;"><a name="_ftn21" href="#_ftnref21">[21]</a> -687.</p>
<p style="text-align: justify;"><a name="_ftn22" href="#_ftnref22">[22]</a> Kurt Cagle, Understanding the Big Data Lifecycle - LinkedIn Pulse (2015)</p>
<p style="text-align: justify;"><a name="_ftn23" href="#_ftnref23">[23]</a> Coyle, Frank P. <em>XML, Web services, and the data revolution</em>. Addison-Wesley Longman Publishing Co., Inc., 2002.</p>
<p style="text-align: justify;"><a name="_ftn24" href="#_ftnref24">[24]</a> Pautasso, Cesare, Olaf Zimmermann, and Frank Leymann. "Restful web services vs. big'web services: making the right architectural decision." <em>Proceedings of the 17th international conference on World Wide Web</em>. ACM, 2008.</p>
<p style="text-align: justify;"><a name="_ftn25" href="#_ftnref25">[25]</a> Banker, Kyle. <em>MongoDB in action</em>. Manning Publications Co., 2011</p>
<p style="text-align: justify;"><a name="_ftn26" href="#_ftnref26">[26]</a> McCreary, Dan, and Ann Kelly. "Making sense of NoSQL." <em>Shelter Island: Manning</em> (2014): 19-20.</p>
<p style="text-align: justify;"><a name="_ftn27" href="#_ftnref27">[27]</a> <em>ibid</em></p>
<p style="text-align: justify;"><a name="_ftn28" href="#_ftnref28">[28]</a> Zhang, Hao, et al. "In-memory big data management and processing: A survey." <em>IEEE Transactions on Knowledge and Data Engineering</em> 27.7 (2015): 1920-1948.</p>
<p style="text-align: justify;"><a name="_ftn29" href="#_ftnref29">[29]</a> <em>ibid</em></p>
<p style="text-align: justify;"><a name="_ftn30" href="#_ftnref30">[30]</a> <em>ibid</em></p>
<p style="text-align: justify;"><a name="_ftn31" href="#_ftnref31">[31]</a> Supra Note 20</p>
<p style="text-align: justify;"><a name="_ftn32" href="#_ftnref32">[32]</a> Ballard, Chuck, et al. <em>IBM solidDB: Delivering Data with Extreme Speed</em>. IBM Redbooks, 2011.</p>
<p style="text-align: justify;"><a name="_ftn33" href="#_ftnref33">[33]</a> Shanahan, James G., and Laing Dai. "Large scale distributed data science using apache spark." <em>Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining</em>. ACM, 2015. <sup>33</sup> Shvachko, Konstantin, et al. "The hadoop distributed file system." <em>2010 IEEE 26th symposium on mass storage systems and technologies (MSST)</em>. IEEE, 2010.</p>
<p style="text-align: justify;"><a name="_ftn34" href="#_ftnref34">[34]</a> Borthakur, Dhruba. "The hadoop distributed file system: Architecture and design." <em>Hadoop Project Website</em></p>
<p style="text-align: justify;"><a name="_ftn35" href="#_ftnref35">[35]</a> .2007 (2007): 21.</p>
<p style="text-align: justify;"><a name="_ftn36" href="#_ftnref36">[36]</a> <em>ibid</em></p>
<p style="text-align: justify;"><a name="_ftn37" href="#_ftnref37">[37]</a> <em>ibid</em></p>
<p style="text-align: justify;"><a name="_ftn38" href="#_ftnref38">[38]</a> Zikopoulos, Paul, and Chris Eaton. <em>Understanding big data: Analytics for enterprise class hadoop and streaming data</em>. McGraw-Hill Osborne Media, 2011.</p>
<p style="text-align: justify;"><a name="_ftn39" href="#_ftnref39">[39]</a> Bizer, Christian, et al. "The meaningful use of big data: four perspectives--four challenges." <em>ACM SIGMOD Record</em> 40.4 (2012): 56-60.</p>
<p style="text-align: justify;"><a name="_ftn40" href="#_ftnref40">[40]</a> Kaisler, Stephen, et al. "Big data: issues and challenges moving forward." <em>System Sciences (HICSS), 2013 46th Hawaii International Conference on</em>. IEEE, 2013.</p>
<p style="text-align: justify;"><a name="_ftn41" href="#_ftnref41">[41]</a> Supra Note 21</p>
<p style="text-align: justify;"><a name="_ftn42" href="#_ftnref42">[42]</a> DuCharme, Bob. "What Do RDF and SPARQL bring to Big Data Projects?." <em>Big Data</em> 1.1 (2013): 38-41.</p>
<p style="text-align: justify;"><a name="_ftn43" href="#_ftnref43">[43]</a> Zhong, Yunqin, et al. "Towards parallel spatial query processing for big spatial data." <em>Parallel and </em></p>
<p style="text-align: justify;"><em>Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International</em>. IEEE, 2012.</p>
<p style="text-align: justify;"><a name="_ftn44" href="#_ftnref44">[44]</a> Ma, Li, et al. "Effective and efficient semantic web data management over DB2." <em>Proceedings of the 2008 ACM SIGMOD international conference on Management of data</em>. ACM, 2008.</p>
<p style="text-align: justify;"><a name="_ftn45" href="#_ftnref45">[45]</a> Lohr, Steve. "The age of big data." <em>New York Times</em> 11 (2012).</p>
<p style="text-align: justify;"><a name="_ftn46" href="#_ftnref46">[46]</a> Pääkkönen, Pekka, and Daniel Pakkala. "Reference architecture and classification of technologies, products and services for big data systems." <em>Big Data Research</em> 2.4 (2015): 166-186.</p>
<p style="text-align: justify;"><a name="_ftn47" href="#_ftnref47">[47]</a> Sumbaly, Roshan, et al. "Serving large-scale batch computed data with project voldemort." <em>Proceedings of the 10th USENIX conference on File and Storage Technologies</em>. USENIX Association, 2012.</p>
<p style="text-align: justify;"><a name="_ftn48" href="#_ftnref48">[48]</a> Bar-Sinai, Michael. "Big Data Technology Literature Review." <em>arXiv preprint arXiv:1506.08978</em> (2015).</p>
<p style="text-align: justify;"><a name="_ftn49" href="#_ftnref49">[49]</a> ibid</p>
<p style="text-align: justify;"><a name="_ftn50" href="#_ftnref50">[50]</a> Condie, Tyson, et al. "MapReduce Online." <em>Nsdi</em>. Vol. 10. No. 4. 2010.</p>
<p style="text-align: justify;"><a name="_ftn51" href="#_ftnref51">[51]</a> Supra Note 47</p>
<p style="text-align: justify;"><a name="_ftn52" href="#_ftnref52">[52]</a> Dean, Jeffrey, and Sanjay Ghemawat. "MapReduce: a flexible data processing tool." <em>Communications of the ACM</em> 53.1 (2010): 72-77.</p>
<p style="text-align: justify;"><a name="_ftn53" href="#_ftnref53">[53]</a> ibid</p>
<p style="text-align: justify;"><a name="_ftn54" href="#_ftnref54">[54]</a> Big Data and Privacy: A Technological Perspective, White House,</p>
<p style="text-align: justify;">https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy__may_2014</p>
<p style="text-align: justify;"><a name="_ftn55" href="#_ftnref55">[55]</a> Tene, Omer, and Jules Polonetsky. "Big data for all: Privacy and user control in the age of analytics." <em>Nw. J. Tech. & Intell. Prop.</em> 11 (2012): xxvii.</p>
<p style="text-align: justify;"><a name="_ftn56" href="#_ftnref56">[56]</a> Big Data and Privacy: A Technological Perspective, White House,</p>
<p style="text-align: justify;">https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy__may_2014</p>
<p style="text-align: justify;"><a name="_ftn57" href="#_ftnref57">[57]</a> Privacy by design in big data, ENISA</p>
<p style="text-align: justify;"><a name="_ftn58" href="#_ftnref58">[58]</a> Big Data and Privacy: A Technological Perspective, White House,</p>
<p style="text-align: justify;">https://www.whitehouse.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy__may_2014</p>
<p style="text-align: justify;"><a name="_ftn59" href="#_ftnref59">[59]</a> Id</p>
<p style="text-align: justify;"><a name="_ftn60" href="#_ftnref60">[60]</a> Id</p>
<p style="text-align: justify;"><a name="_ftn61" href="#_ftnref61">[61]</a> Tene, Omer, and Jules Polonetsky. "Privacy in the age of big data: a time for big decisions." <em>Stanford Law Review Online</em> 64 (2012): 63.</p>
<p style="text-align: justify;"><a name="_ftn62" href="#_ftnref62">[62]</a> Lane, Julia, et al., eds. <em>Privacy, big data, and the public good: Frameworks for engagement</em>. Cambridge University Press, 2014.</p>
<p style="text-align: justify;"><a name="_ftn63" href="#_ftnref63">[63]</a> Crawford, Kate, and Jason Schultz. "Big data and due process: Toward a framework to redress predictive privacy harms." <em>BCL Rev.</em> 55 (2014): 93.</p>
<p style="text-align: justify;"><a name="_ftn64" href="#_ftnref64">[64]</a> http://homes.esat.kuleuven.be/~sguerses/papers/DanezisGuersesSurveillancePets2010.pdf</p>
<p style="text-align: justify;"><a name="_ftn65" href="#_ftnref65">[65]</a> Seda Gurses and George Danezis, A critical review of 10 years of privacy technology, August 12th 2010, http://homes.esat.kuleuven.be/~sguerses/papers/DanezisGuersesSurveillancePets2010.pdf</p>
<p style="text-align: justify;"><a name="_ftn66" href="#_ftnref66">[66]</a> Id</p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/technology-behind-big-data'>http://editors.cis-india.org/internet-governance/blog/technology-behind-big-data</a>
</p>
No publisherGeethanjali Jujjavarapu and Udbhav TiwariBig DataPrivacyInternet GovernanceFeaturedHomepage2016-12-04T09:53:43ZBlog EntryBig Data in India: Benefits, Harms, and Human Rights - Workshop Report
http://editors.cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report
<b>The Centre for Internet and Society held a one-day workshop on “Big Data in India: Benefits, Harms and Human Rights” at India Habitat Centre, New Delhi on the 1st of October, 2016. This report is a compilation of the the issues discussed, ideas exchanged and challenges recognized during the workshop. The objective of the workshop was to discuss aspects of big data technologies in terms of harms, opportunities and human rights. The discussion was designed around an extensive study of current and potential future uses of big data for governance in India, that CIS has undertaken over the last year with support from the MacArthur Foundation.</b>
<p> </p>
<p><strong>Contents</strong></p>
<p><a href="#1"><strong>Big Data: Definitions and Global South Perspectives</strong></a></p>
<p><a href="#2"><strong>Aadhaar as Big Data</strong></a></p>
<p><a href="#3"><strong>Seeding</strong></a></p>
<p><a href="#4"><strong>Aadhaar and Data Security</strong></a></p>
<p><a href="#5"><strong>Aadhaar’s Relational Arrangement with Big Data Scheme</strong></a></p>
<p><a href="#6"><strong>The Myths surrounding Aadhaar</strong></a></p>
<p><a href="#7"><strong>IndiaStack and FinTech Apps</strong></a></p>
<p><a href="#8"><strong>Problems with UID</strong></a></p>
<hr />
<h2 id="1">Big Data: Definitions and Global South Perspectives</h2>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">“Big Data” has been defined by multiple scholars till date. The first consideration at the workshop was to discuss various definitions of big data, and also to understand what could be considered Big Data in terms of governance, especially in the absence of academic consensus. One of the most basic ways to define it, as given by the National Institute of Standards and Technology, USA, is to take it to be the data that is beyond the computational capacity of current systems. This definition has been accepted by the UIDAI of India. Another participant pointed out that Big Data is not only indicative of size, but rather the nature of data which is unstructured, and continuously flowing. The Gartner definition of Big Data relies on the three Vs i.e. Volume (size), Velocity (infinite number of ways in which data is being continuously collected) and Variety (the number of ways in which data can be collected in rows and columns).</p>
<p style="text-align: justify;" dir="ltr">The presentation also looked at ways in which Big Data is different from traditional data. It was pointed out that it can accommodate diverse unstructured datasets, and it is ‘relational’ i.e. it needs the presence of common field(s) across datasets which allows these fields to be conjoined. For e.g., the UID in India is being linked to many different datasets, and they don’t constitute Big Data separately, but do so together. An increasingly popular definition is to define data as “Big Data” based on what can be achieved through it. It has been described by authors as the ability to harness new kinds of insight which can inform decision making. It was pointed out that CIS does not subscribe to any particular definition, and is still in the process of coming up with a comprehensive definition of Big Data.</p>
<p style="text-align: justify;" dir="ltr">Further, discussion touched upon the approach to Big Data in the Global South. It was pointed out that most discussions about Big Data in the Global South are about the kind of value that it can have, the ways in which it can change our society. The Global North, on the other hand, has moved on to discussing the ethics and privacy issues associated with Big Data.</p>
<p style="text-align: justify;" dir="ltr">After this, the presentation focussed on case studies surrounding key Central Government initiatives and projects like Aadhaar, Predictive Policing, and Financial Technology (FinTech).</p>
<h2 id="2">Aadhaar as Big Data</h2>
<p style="text-align: justify;" dir="ltr">In presenting CIS’ case study on Aadhaar, it was pointed out that initially, Aadhaar, with its enrollment dataset was by itself being seen as Big Data. However, upon careful consideration in light of definitions discussed above, it can be seen as something that enables Big Data. The different e-governance projects within Digital India, along with Aadhaar, constitute Big Data. The case study discussed the Big Data implications of Aadhaar, and in particular looked at a ‘cradle to grave’ identity mapping through various e-government projects and the datafication of various transaction generated data.</p>
<h2 id="3">Seeding</h2>
<p style="text-align: justify;" dir="ltr">Any digital identity like Aadhaar typically has three features: 1. Identification i.e. a number or card used to identify yourself; 2. Authentication, which is based on your number or card and any other digital attributes that you might have; 3. Authorisation: As bearers of the digital identity, we can authorise the service providers to take some steps on our behalf. The case study discussed ‘seeding’ which enables the Big Data aspects of Digital India. In the process of seeding, different government databases can be seeded with the UID number using a platform called Ginger. Due to this, other databases can be connected to UIDAI, and through it, data from other databases can be queried by using your Aadhaar identity itself. This is an example of relationality, where fractured data is being brought together. At the moment, it is not clear whether this access by UIDAI means that an actual physical copy of such data from various sources will be transferred to UIDAI’s servers or if they will just access it through internet, but the data remains on the host government agency’s server. An example of even private parties becoming a part of this infrastructure was raised by a participant when it was pointed out that Reliance Jio is now asking for fingerprints. This can then be connected to the relational infrastructure being created by UIDAI. The discussion then focused on how such a structure will function, where it was mentioned that as of now, it cannot be said with certainty that UIDAI will be the agency managing this relational infrastructure in the long run, even though it is the one building it.</p>
<h2 id="4">Aadhaar and Data Security</h2>
<p style="text-align: justify;" dir="ltr">This case study also dealt with the sheer lack of data protection legislation in India except for S.43A of the IT Act. The section does not provide adequate protection as the constitutionality of the rules and regulations under S.43A is ambivalent. More importantly, it only refers to private bodies. Hence, any seeding which is being done by the government is outside the scope of data protection legislation. Thus, at the moment, no legal framework covers the processes and the structures being used for datasets. Due to the inapplicability of S.43A to public bodies, questions were raised as to the existence of a comprehensive data protection policy for government institutions. Participants answered the question in the negative. They pointed out that if any government department starts collecting data, they develop their own privacy policy. There are no set guidelines for such policies and they do not address concerns related to consent, data minimisation and purpose limitation at all. Questions were also raised about the access and control over Big Data with government institutions. A tentative answer from a participant was that such data will remain under the control of the domain specific government ministry or department, for e.g. MNREGA data with the Ministry of Rural Development, because the focus is not on data centralisation but rather on data linking. As long as such fractured data is linked and there is an agency that is responsible to link them, this data can be brought together. Such data is primarily for government agencies. But the government is opening up certain aspects of the data present with it for public consumption for research and entrepreneurial purposes.The UIDAI provides you access to your own data after paying a minimal fee. The procedure for such access is still developing.</p>
<h2 id="5">Aadhaar’s Relational Arrangement with Big Data Scheme</h2>
<p style="text-align: justify;" dir="ltr">The various Digital India schemes brought in by the government were elucidated during the workshop. It was pointed out that these schemes extend to myriad aspects of a citizen’s daily life and cover all the essential public services like health, education etc. This makes Aadhaar imperative even though the Supreme Court has observed that it is not mandatory for every citizen to have a unique identity number. The benefits of such identity mapping and the ecosystem being generated by it was also enumerated during the discourse. But the complete absence of any data ethics or data confidentiality principles make us unaware of the costs at which these benefits are being conferred on us. Apart from surveillance concerns, the knowledge gap being created between the citizens and the government was also flagged. Three main benefits touted to be provided by Aadhaar were then analysed. The first is the efficient delivery of services. This appears to be an overblown claim as the Aadhaar specific digitisation and automation does not affect the way in which employment will be provided to citizens through MNREGA or how wage payment delays will be overcome. These are administrative problems that Aadhaar and associated technologies cannot solve. The second is convenience to the citizens. The fallacies in this assertion were also brought out and identified. Before the Aadhaar scheme was rolled in, ration cards were issued based on certain exclusion and inclusion criteria.. The exclusion and inclusion criteria remain the same while another hurdle in the form of Aadhaar has been created. As India is still lacking in supporting infrastructure such as electricity, server connectivity among other things, Aadhaar is acting as a barrier rather than making it convenient for citizens to enroll in such schemes.The third benefit is fraud management. Here, a participant pointed out that this benefit was due to digitisation in the form of GPS chips in food delivery trucks and electronic payment and not the relational nature of Aadhaar. Aadhaar is only concerned with the linking up or relational part. About deduplication, it was pointed out how various government agencies have tackled it quite successfully by using technology different from biometrics which is unreliable at the best of times.</p>
<h2 id="6">The Myths surrounding Aadhaar</h2>
<p style="text-align: justify;" dir="ltr">The discussion also reflected on the fact that Aadhaar is often considered to be a panacea that subsumes all kinds of technologies to tackle leakages. However, this does not take into account the fact that leakages happen in many ways. A system should have been built to tackle those specific kinds of leakages, but the focus is solely on Aadhaar as the cure for all. Notably, participants who have been a part of the government pointed out how this myth is misleading and should instead be seen as the first step towards a more digitally enhanced country which is combining different technologies through one medium.</p>
<h2 id="7">IndiaStack and FinTech Apps</h2>
<h3 id="71">What is India Stack?</h3>
<p style="text-align: justify;" dir="ltr">The focus then shifted to another extremely important Big Data project, India Stack, being conceptualised and developed by a team of private developers called iStack, for the NPCI. It builds on the UID project, Jan Dhan Yojana and mobile services trinity to propagate and develop a cashless, presence-less, paperless and granular consent layer based on UID infrastructure to digitise India.</p>
<p style="text-align: justify;" dir="ltr">A participant pointed out that the idea of India Stack is to use UID as a platform and keep stacking things on it, such that more and more applications are developed. This in turn will help us to move from being a ‘data poor’ country to a ‘data rich’ one. The economic benefits of this data though as evidenced from the TAGUP report - a report about the creation of National Information Utilities to manage the data that is present with the government - is for the corporations and not the common man. The TAGUP report openly talks about privatisation of data.</p>
<h3 id="72">Problems with India Stack</h3>
<p style="text-align: justify;" dir="ltr">The granular consent layer of India Stack hasn’t been developed yet but they have proposed to base it on MIT Media Lab’s OpenPDS system. The idea being that, on the basis of the choices made by the concerned person, access to a person’s personal information may be granted to an agency like a bank. What is more revolutionary is that India Stack might even revoke this access if the concerned person expresses a wish to do so or the surrounding circumstances signal to India Stack that it will be prudent to do so. It should be pointed out that the the technology required for OpenPDS is extremely complex and is not available in India. Moreover, it’s not clear how this system would work. Apart from this, even the paperless layer has its faults and has been criticised by many since its inception, because an actual government signed and stamped paper has been the basis of a claim.. In the paperless system, you are provided a Digilocker in which all your papers are stored electronically, on the basis of your UID number. However, it was brought to light that this doesn’t take into account those who either do not want a Digilocker or UID number or cases where they do not have access to their digital records. How in such cases will people make claims?</p>
<h3 id="73">A Digital Post-Dated Cheque: It’s Ramifications</h3>
<p style="text-align: justify;" dir="ltr">A key change that FinTech apps and the surrounding ecosystem want to make is to create a digital post-dated cheque so as to allow individuals to get loans from their mobiles especially in remote areas. This will potentially cut out the need to construct new banks, thus reducing the capital expenditure , while at the same time allowing the credit services to grow. The direct transfer of money between UID numbers without the involvement of banks is a step to further help this ecosystem grow. Once an individual consents to such a system, however, automatic transfer of money from one’s bank accounts will be affected, regardless of the reason for payment. This is different from auto debt deductions done by banks presently, as in the present system banks have other forms of collateral as well. The automatic deduction now is only affected if these other forms are defaulted upon. There is no knowledge as to whether this consent will be reversible or irreversible. As Jan Dhan Yojana accounts are zero balance accounts, the account holder will be bled dry. The implication of schemes such as “Loan in under 8 minutes” were also discussed. The advantage of such schemes is that transaction costs are reduced.The financial institution can thus grant loans for the minimum amount without any additional enquiries. It was pointed out that this new system is based on living on future income much like the US housing bubble crash. Interestingly, in Public Distribution Systems, biometrics are insisted upon even though it disrupts the system. This can be seen as a part of the larger infrastructure to ensure that digital post-dated cheques become a success.</p>
<h3 id="74">The Role of FinTech Apps</h3>
<p style="text-align: justify;" dir="ltr">FinTech ‘apps’ are being presented with the aim of propagating financial inclusion. The Technology Advisory Group for Unique Projects report stated that as managing such information sources is a big task, just like electricity utilities, a National Information Utilities (NIU) should be set up for data sources. These NIUs as per the report will follow a fee based model where they will be charging for their services for government schemes. The report identified two key NIUs namely the National Payments Corporation of India (NPCI) and the Goods and Services Tax Network (GSTN). The key usage that FinTech applications will serve is credit scoring. The traditional credit scoring data sources only comprised a thin file of records for an individual, but the data that FinTech apps collect - a person’s UID number, mobile number. and bank account number all linked up, allow for a far more comprehensive credit rating. Government departments are willing to share this data with FinTech apps as they are getting analysis in return. Thus, by using UID and the varied data sources that have been linked together by UID, a ‘thick file’ is now being created by FinTech apps. Banking apps have not yet gone down the route of FinTech apps to utilise Big Data for credit scoring purposes.</p>
<p style="text-align: justify;" dir="ltr"> </p>
<p style="text-align: justify;" dir="ltr">The two main problems with such apps is that there is no uniform way of credit scoring. This distorts the rate at which a person has to pay interest. The consent layer adds another layer of complication as refusal to share mobile data with a FinTech app may lead to the app declaring one to be a risky investment thus, subjecting that individual to a higher rate of interest .</p>
<div style="text-align: justify;" dir="ltr"> </div>
<h3 id="75">Regulation of FinTech Apps and the UID Infrastructure</h3>
<p style="text-align: justify;" dir="ltr"> India Stack and the applications that are being built on it, generate a lot of transaction metadata that is very intimate in nature. The privacy aspects of the UID legislation doesn't cover such data. The granular consent layer which has been touted to cover this still has to come into existence. Also, Big Data is based on sharing and linking of data. Here, privacy concerns and Big Data objectives clash. Big Data by its very nature challenges privacy principles like data minimisation and purpose limitation.The need for regulation to cover the various new apps and infrastructure which are being developed was pointed out.</p>
<h2 id="8">Problems with UID</h2>
<p style="text-align: justify;" dir="ltr">It has been observed that any problem present with Aadhaar is usually labelled as a teething problem, it’s claimed that it will be solved in the next 10 years. But, this begs the question - why is the system online right now?</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Aadhaar is essentially a new data condition and a new exclusion or inclusion criteria. Data exclusion modalities as observed in Rajasthan after the introduction of biometric Point of Service (POS) machines at ration shops was found to be 45% of the population availing PDS services. This number also includes those who were excluded from the database by being included in the wrong dataset. There is no information present to tell us how many actual duplicates and how many genuine ration card holders were weeded out/excluded by POS.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">It was also mentioned that any attempt to question Aadhaar is considered to be an attempt to go back to the manual system and this binary thinking needs to change. Big Data has the potential to benefit people, as has been evidenced by the scholarship and pension portals. However, Big Data’s problems arise in systems like PDS, where there is centralised exclusion at the level of the cloud. Moreover, the quantity problem present in the PDS and MNREGA systems persists. There is still the possibility of getting lesser grains and salary even with analysis of biometrics, hence proving that there are better technologies to tackle these problems. Presently, the accountability mechanisms are being weakened as the poor don’t know where to go to for redressal. Moreover, the mechanisms to check whether the people excluded are duplicates or not is not there. At the time of UID enrollment, out of 90 crores, 9 crore were rejected. There was no feedback or follow-up mechanism to figure out why are people being rejected. It was just assumed that they might have been duplicates.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Another problem is the rolling out of software without checking for inefficiencies or problems at a beta testing phase. The control of developers over this software, is so massive that it can be changed so easily without any accountability.. The decision making components of the software are all proprietary like in the the de-duplication algorithm being used by the UIDAI. Thus, this leads to a loss of accountability because the system itself is in flux, none of it is present in public domain and there are no means to analyse it in a transparent fashion..</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">These schemes are also being pushed through due to database politics. On a field study of NPR of citizens, another Big Data scheme, it was found that you are assumed to be an alien if you did not have the documents to prove that you are a citizen. Hence, unless you fulfill certain conditions of a database, you are excluded and are not eligible for the benefits that being on the database afford you.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Why is the private sector pushing for UIDAI and the surrounding ecosystem?</p>
<p style="text-align: justify;" dir="ltr">Financial institutions stand to gain from encouraging the UID as it encourages the credit culture and reduces transaction costs.. Another advantage for the private sector is perhaps the more obvious one, that is allows for efficient marketing of products and services..</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">The above mentioned fears and challenges were actually observed on the ground and the same was shown through the medium of a case study in West Bengal on the smart meters being installed there by the state electricity utility. While the data coming in from these smart meters is being used to ensure that a more efficient system is developed,it is also being used as a surrogate for income mapping on the basis of electricity bills being paid. This helps companies profile neighbourhoods. The technical officer who first receives that data has complete control over it and he can easily misuse the data. This case study again shows that instruments like Aadhaar and India Stack are limited in their application and aren’t the panacea that they are portrayed to be.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">A participant pointed out that in the light of the above discussions, the aim appears to be to get all kinds of data, through any source, and once you have gotten the UID, you link all of this data to the UID number, and then use it in all the corporate schemes that are being started. Most of the problems associated with Big Data are being described as teething problems. The India Stack and FinTech scheme is coming in when we already know about the problems being faced by UID. The same problems will be faced by India Stack as well.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Can you opt out of the Aadhaar system and the surrounding ecosystem?</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">The discussion then turned towards whether there can be voluntary opting out from Aadhaar. It was pointed out that the government has stated that you cannot opt out of Aadhaar. Further, the privacy principles in the UIDAI bill are ambiguously worded where individuals only have recourse for basic things like correction of your personal information. The enforcement mechanism present in the UIDAI Act is also severely deficient. There is no notification procedure if a data breach occurs. . The appellate body ‘Cyber Appellate Tribunal’ has not been set up in three years.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">CCTNS: Big Data and its Predictive Uses</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">What is Predictive Policing?</p>
<p style="text-align: justify;" dir="ltr">The next big Big Data case study was on the Crime and Criminal Tracking Network & Systems (CCTNS). Originally it was supposed to be a digitisation and interconnection scheme where police records would be digitised and police stations across the length and breadth of the country would be interconnected. But, in the last few years some police departments of states like Chandigarh, Delhi and Jharkhand have mooted the idea of moving on to predictive policing techniques. It envisages the use of existing statistical and actuarial techniques along with many other tropes of data to do so. It works in four ways: 1. By predicting the place and time where crimes might occur; 2. To predict potential future offenders; 3. To create profiles of past crimes in order to predict future crimes; 4. Predicting groups of individuals who are likely to be victims of future crimes.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">How is Predictive Policing done?</p>
<p style="text-align: justify;" dir="ltr">To achieve this, the following process is followed: 1. Data collection from various sources which includes structured data like FIRs and unstructured data like call detail records, neighbourhood data, crime seasonal patterns etc. 2. Analysis by using theories like the near repeat theory, regression models on the basis of risk factors etc. 3. Intervention</p>
<div style="text-align: justify;" dir="ltr"> </div>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Flaws in Predictive Policing and questions of bias</p>
<p style="text-align: justify;" dir="ltr">An obvious weak point in the system is that if the initial data going into the system is wrong or biased, the analysis will also be wrong. Efforts are being made to detect such biases. An important way to do so will be by building data collection practices into the system that protect its accuracy. The historical data being entered into the system is carrying on the prejudices inherited from the British Raj and biases based on religion, caste, socio-economic background etc.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">One participant brought about the issue of data digitization in police stations, and the impact of this haphazard, unreliable data on a Big Data system. This coupled with paucity of data is bound to lead to arbitrary results. An effective example was that of black neighbourhoods in the USA. These are considered problematic and thus they are policed more, leading to a higher crime rate as they are arrested for doing things that white people in an affluent neighbourhood get away with. This in turn further perpetuates the crime rate and it becomes a self-fulfilling prophecy. In India, such a phenomenon might easily develop in the case of migrants, de-notified tribes, Muslims etc. A counter-view on bias and discrimination was offered here. One participant pointed out that problems with haphazard or poor quality of data is not a colossal issue as private companies are willing to fill this void and are actually doing so in exchange for access to this raw data. It was also pointed out how bias by itself is being used as an all encompassing term. There are multiplicities of biases and while analysing the data, care should be taken to keep it in mind that one person’s bias and analysis might and usually does differ from another. Even after a computer has analysed the data, the data still falls into human hands for implementation.</p>
<p style="text-align: justify;" dir="ltr">The issue of such databases being used to target particular communities on the basis of religion, race, caste, ethnicity among other parameters was raised. Questions about control and analysis of data were also discussed, i.e. whether it will be top-down with data analysis being done in state capitals or will this analysis be done at village and thana levels as well too. It was discussed as topointed out how this could play a major role in the success and possible persecutory treatment of citizens, as the policemen at both these levels will have different perceptions of what the data is saying. . It was further pointed out, that at the moment, there’s no clarity on the mode of implementation of Big Data policing systems. Police in the USA have been seen to rely on Big Data so much that they have been seen to become ‘data myopic’. For those who are on the bad side of Big Data, in the Indian context, laws like preventive detention can be heavily misused.There’s a very high chance that predictive policing due to the inherent biases in the system and the prejudices and inefficiency of the legal system will further suppress the already targeted sections of the society. A counterpoint was raised and it was suggested that contrary to our fears, CCTNS might lead to changes in our understanding and help us to overcome longstanding biases.</p>
<p style="text-align: justify;" dir="ltr">Open Knowledge Architecture as a solution to Big Data biases?</p>
<p style="text-align: justify;" dir="ltr">The conference then mulled over the use of ‘Open Knowledge’ architecture to see whether it can provide the solution to rid Big Data of its biases and inaccuracies if enough eyes are there. It was pointed out that Open Knowledge itself can’t provide foolproof protection against these biases as the people who make up the eyes themselves are predominantly male belonging to the affluent sections of the society and they themselves suffer from these biases.</p>
<p style="text-align: justify;" dir="ltr">Who exactly is Big Data supposed to serve?</p>
<p style="text-align: justify;" dir="ltr">The discussion also looked at questions such as who is this data for? Janata Information System (JIS), is a concept developed by MKSS where the data collected and generated by the government is taken to be for the common citizens. For e.g. MNREGA data should be used to serve the purposes of the labourers. The raw data as is available at the moment, usually cannot be used by the common man as it is so vast and full of information that is not useful for them at all. It was pointed out that while using Big Data for policy planning purposes, the actual string of information that turned out to be needed was very little but the task of unravelling this data for civil society purposes is humongous. By presenting the data in the right manner, the individual can be empowered. The importance of data presentation was also flagged. It was agreed upon that the content of the data should be for the labourer and not a MNC, as the MNC has the capability to utilise the raw data on it’s own regardless.</p>
<p style="text-align: justify;" dir="ltr">Concerns about Big Data usage</p>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Participants pointed out that privacy concerns are usually brushed under the table due to a belief that the law is sufficient or that the privacy battle has already been lost. </p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">In the absence of knowledge of domain and context, Big Data analysis is quite limited. Big Data’s accuracy and potential to solve problems needs to be factually backed.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The narrative of Big Data often rests on the assumption that descriptive statistics take over inferential statistics, thus eliminating the need for domain specific knowledge. It is claimed that the data is so big that it will describe everything that we need to know.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Big Data is creating a shift from a deductive model of scientific rigour to an inductive one. In response to this, a participant offered the idea that troves of good data allow us to make informed questions on the basis of which the deductive model will be formed. A hybrid approach combining both deductive and inductive might serve us best.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The need to collect the right data in the correct format, in the right place was also expressed.</p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Potential Research Questions & Participants’ Areas of Research</p>
<p style="text-align: justify;" dir="ltr">Following this discussion, participants brainstormed to come up with potential areas of research and research questions. They have been captured below:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Big Data, Aadhaar and India Stack:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Has Aadhaar been able to tackle illegal ways of claiming services or are local negotiations and other methods still prevalent?</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Is the consent layer of India Stack being developed in a way that provides an opportunity to the UID user to give informed consent? The OpenPDS and its counterpart in the EU i.e. the My Data Structure were designed for countries with strong privacy laws. Importantly, they were meant for information shared on social media and not for an individual’s health or credit history. India is using it in a completely different sphere without strong data protection laws. What were the granular consent layer structures present in the West designed for and what were they supposed to protect?</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The question of ownership of data needs to be studied especially in context of a globalised world where MNCs are collecting copious amounts of data of Indian citizens. What is the interaction of private parties in this regard?</p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Big Data and Predictive Policing:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">How are inequalities being created through the Big Data systems? Lessons should be taken from the Western experience with the advent of predictive policing and other big data techniques - they tend to lead to perpetuation of the current biases which are already ingrained in the system.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">It was also pointed out how while studying these topics and anything related to technology generally, we become aware of a divide that is present between the computational sciences and social sciences. This divide needs to be erased if Big Data or any kind of data is to be used efficiently. There should be a cross-pollination between different groups of academics. An example of this can be seen to be the ‘computational social sciences departments’ that have been coming up in the last 3-4 years.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Why are so many interim promises made by Big Data failing? A study of this phenomenon needs to be done from a social science perspective. This will allow one to look at it from a different angle.</p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Studying Big Data:</p>
<div style="text-align: justify;" dir="ltr"> </div>
<ol><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">What is the historical context of the terms of reference being used for Big Data? The current Big Data debate in India is based on parameters set by the West. For better understanding of Big Data, it was suggested that P.C. Mahalanobis’ experience while conducting the Indian census, (which was the Big Data of that time) can be looked at to get a historical perspective on Big Data. This comparison might allow us to discover questions that are important in the Indian context. It was also suggested that rather than using ‘Big Data’ as a catchphrase to describe these new technological innovations, we need to be more discerning.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">What are the ideological aspects that must be considered while studying Big Data? What does the dialectical promise of technology mean? It was contended that every time there is a shift in technology, the zeitgeist of that period is extremely excited and there are claims that it will solve everything. There’s a need to study this dialectical promise and the social promise surrounding it.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">Apart from the legitimate fears that Big Data might lead to exclusion, what are the possibilities in which it improve inclusion too?</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">The diminishing barrier between the public and private self, which is a tangent to the larger public-private debate was mentioned.</p>
</li><li style="list-style-type: decimal;" dir="ltr">
<p style="text-align: justify;" dir="ltr">How does one distinguish between technology failure and process failure while studying Big Data? </p>
</li></ol>
<div style="text-align: justify;" dir="ltr"> </div>
<div style="text-align: justify;" dir="ltr"> </div>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Big Data: A Friend?</p>
<p style="text-align: justify;" dir="ltr">In the concluding session, the fact that the Big Data moment cannot be wished away was acknowledged. The use of analytics and predictive modelling by the private sector is now commonplace and India has made a move towards a database state through UID and Digital India. The need for a nuanced debate, that does away with the false equivalence of being either a Big Data enthusiast or a luddite is crucial.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">A participant offered two approaches to solving a Big Data problem. The first was the Big Data due process framework which states that if a decision has been taken that impacts the rights of a citizen, it needs to be cross examined. The efficacy and practicality of such an approach is still not clear. The second, slightly paternalistic in nature, was the approach where Big Data problems would be solved at the data science level itself. This is much like the affirmative algorithmic approach which says that if in a particular dataset, the data for the minority community is not available then it should be artificially introduced in the dataset. It was also suggested that carefully calibrated free market competition can be used to regulate Big Data. For e.g. a private personal wallet company that charges higher, but does not share your data at all can be an example of such competition. </p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">Another important observation was the need to understand Big Data in a Global South context and account for unique challenges that arise. While the convenience of Big Data is promising, its actual manifestation depends on externalities like connectivity, accurate and adequate data etc that must be studied in the Global South.</p>
<div style="text-align: justify;" dir="ltr"> </div>
<p style="text-align: justify;" dir="ltr">While the promises of Big Data are encouraging, it is also important to examine its impacts and its interaction with people's rights. Regulatory solutions to mitigate the harms of big data while also reaping its benefits need to evolve.</p>
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<p><span id="docs-internal-guid-90fa226f-6157-27d9-30cd-050bdc280875"></span></p>
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<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report'>http://editors.cis-india.org/internet-governance/big-data-in-india-benefits-harms-and-human-rights-a-report</a>
</p>
No publisherVidushi Marda, Akash Deep Singh and Geethanjali JujjavarapuHuman RightsUIDBig DataPrivacyArtificial IntelligenceInternet GovernanceMachine LearningFeaturedDigital IndiaAadhaarInformation TechnologyE-Governance2016-11-18T12:58:19ZBlog EntryPrivacy after Big Data: Compilation of Early Research
http://editors.cis-india.org/internet-governance/blog/privacy-after-big-data-compilation-of-early-research
<b>Evolving data science, technologies, techniques, and practices, including big data, are enabling shifts in how the public and private sectors carry out their functions and responsibilities, deliver services, and facilitate innovative production and service models to emerge. In this compilation we have put together a series of articles that we have developed as we explore the impacts – positive and negative – of big data. This is a growing body of research that we are exploring and
is relevant to multiple areas of our work including privacy and surveillance. Feedback and comments on the compilation are welcome and appreciated.</b>
<p> </p>
<h4><a href="https://github.com/cis-india/website/raw/master/docs/CIS_PrivacyAfterBigData_CompilationOfEarlyResearch_2016.11.pdf">Download the Compilation</a> (PDF)</h4>
<hr />
<h3><strong>Privacy after Big Data</strong></h3>
<p>Evolving data science, technologies, techniques, and practices, including big data, are enabling shifts in how the public and private sectors carry out their functions and responsibilities, deliver services, and facilitate innovative production and service models to emerge. For example, in the public sector, the Indian government has considered replacing the traditional poverty line with targeted subsidies based on individual household income and assets. The my.gov.in platform is aimed to enable participation of the connected citizens, to pull in online public opinion in a structured manner on key governance topics in the country. The 100 Smart Cities Mission looks forwards to leverage big data analytics and techniques to deliver services and govern citizens within city sub-systems. In the private sector, emerging financial technology companies are developing credit scoring models using big, small, social, and fragmented data so that people with no formal credit history can be offered loans. These models promote efficiency and reduction in cost through personalization and are powered by a wide variety of data sources including mobile data, social media data, web usage data, and passively collected data from usages of IoT or connected devices.</p>
<p>These data technologies and solutions are enabling business models that are based on the ideals of ‘less’: cash-less, presence-less, and paper-less. This push towards an economy premised upon a foundational digital ID in a prevailing condition of absent legal frameworks leads to substantive loss of anonymity and privacy of individual citizens and consumers vis-a-vis both the state and the private sector. Indeed, the present use of these techniques run contrary to the notion of the ‘sunlight effect’ - making the individual fully transparent (often without their knowledge) to the state and private sector, while the algorithms and means of reaching a decision are opaque and inaccessible to the individual.</p>
<p>These techniques, characterized by the volume of data processed, the variety of sources data is processed from, and the ability to both contextualize - learning new insights from disconnected data points - and de-contextualize - finding correlation rather than causation - have also increased the value of all forms of data. In some ways, big data has made data exist on an equal playing field as far as monetisation and joining up are concerned. Meta data can be just as valuable to an entity as content data. As data science techniques evolve to find new ways of collecting, processing, and analyzing data - the benefits of the same are clear and tangible, while the harms are less clear, but significantly present.</p>
<p>Is it possible for an algorithm to discriminate? Will incorrect decisions be made based on data collected? Will populations be excluded from necessary services if they do not engage with certain models or do emerging models overlook certain populations? Can such tools be used to surveil individuals at a level of granularity that was formerly not possible and before a crime occurs? Can such tools be used to violate rights – for example target certain types of speech or groups online? And importantly, when these practices are opaque to the individual, how can one seek appropriate and effective remedy.</p>
<p>Traditionally, data protection standards have defined and established protections for certain categories of data. Yet, data science techniques have evolved beyond data protection principles. It is now infinitely harder to obtain informed consent from an individual when data that is collected can be used for multiple purposes by multiple bodies. Providing notice for every use is also more difficult – as is fulfilling requirements of data minimization. Some say privacy is dead in the era of big data. Others say privacy needs to be re-conceptualized, while others say protecting privacy now, more than ever, requires a ‘regulatory sandbox’ that brings together technical design, markets, legislative reforms, self regulation, and innovative regulatory frameworks. It also demands an expanding of the narrative around privacy – one that has largely been focused on harms such as misuse of data or unauthorized collection – to include discrimination, marginalization, and competition harms.</p>
<p>In this compilation we have put together a series of articles that we have developed as we explore the impacts – positive and negative – of big data. This includes looking at India’s data protection regime in the context of big data, reviewing literature on the benefits of harms of big data, studying emerging predictive policing techniques that rely on big data, and analyzing closely the impact of big data on specific privacy principles such as consent. This is a growing body of research that we are exploring and is relevant to multiple areas of our work including privacy and surveillance. Feedback and comments on the compilation are welcome and appreciated.</p>
<p><em>Elonnai Hickok</em><br />Director - Internet Governance</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/privacy-after-big-data-compilation-of-early-research'>http://editors.cis-india.org/internet-governance/blog/privacy-after-big-data-compilation-of-early-research</a>
</p>
No publisherSaumyaa NaiduHuman RightsIT ActBig DataPrivacyInternet GovernanceSmart CitiesData ProtectionInformation TechnologyPublications2016-11-12T01:37:03ZBlog EntryWorkshop on 'Privacy after Big Data' (Delhi, November 12)
http://editors.cis-india.org/internet-governance/events/privacy-after-big-data-delhi-nov-12-2016
<b>The Centre for Internet and Society (CIS) and the Sarai programme, CSDS, invite you to a workshop on 'Privacy after Big Data: What Changes? What should Change?' on Saturday, November 12. This workshop aims to build a dialogue around some of the key government-led big data initiatives in India and elsewhere that are contributing significant new challenges and concerns to the ongoing debates on the right to privacy. It is an open event. Please register to participate.</b>
<p> </p>
<h4>Invitation note and agenda: <a href="https://github.com/cis-india/website/raw/master/docs/CIS-Sarai_PrivacyAfterBigData_ConceptAgenda.pdf">Download</a> (PDF)</h4>
<hr />
<h3>Venue and RSVP</h3>
<p><strong>Venue:</strong> Centre for the Study of Developing Societies 29, Rajpur Road, Civil Lines, Delhi 110054.</p>
<p><strong>Location on Google Maps:</strong> <a href="https://www.google.com/maps/place/CSDS/@28.677775,77.2162523,17z/">https://www.google.com/maps/place/CSDS/@28.677775,77.2162523,17z/</a>.</p>
<p><strong>Registration:</strong> <a href="https://goo.gl/forms/py0Q0u8rMppu4smE3">Complete this form</a>.</p>
<h3>Concept Note</h3>
<p>In this age of big data, discussions about privacy are intertwined with the use of technology and the data deluge. Though big data possesses enormous value for driving innovation and contributing to productivity and efficiency, privacy concerns have gained significance in the dialogue around regulated use of data and the means by which individual privacy might be compromised through means such as surveillance, or protected. The tremendous opportunities big data creates in varied sectors ranges from financial technology, governance, education, health, welfare schemes, smart cities to name a few.</p>
<p>With the UID (“Aadhaar”) project re-animating the Right to Privacy debate in India, and the financial technology ecosystem growing rapidly, striking a balance between benefits of big data and privacy concerns is a critical policy question that demands public dialogue and research to inform an evidence based decision.</p>
<p>Also, with the advent of potential big data initiatives like the ambitious Smart Cities Mission under the Digital India Scheme, which would rely on harvesting large data sets and the use of analytics in city subsystems to make public utilities and services efficient, the tasks of ensuring data security on one hand and protecting individual privacy on the other become harder.</p>
<p>As key privacy principles are at loggerheads with big data activities, it is important to consider privacy as an embedded component in the processes, systems and projects, rather than being considered as an afterthought. These examples highlight the current state of discourse around data protection and privacy in India and the shapes they are likely to take in near future.</p>
<p>This workshop aims to build a dialogue around some of the key government-led big data initiatives in India and elsewhere that are contributing significant new challenges and concerns to the ongoing debates on the right to privacy.</p>
<h3>Agenda</h3>
<h4>09:00-09:30 Tea and Coffee</h4>
<h4>09:30-10:00 Introduction</h4>
<p><a href="#amber">Mr. Amber Sinha</a> and <a href="#sandeep">Mr. Sandeep Mertia</a><br />
<em>This session will introduce the topic of the workshop in the context of the ongoing works at CIS and Sarai.</em></p>
<h4>10:00-11:00 From Privacy Bill(s) to ‘Habeas Data’</h4>
<p><a href="#usha">Dr. Usha Ramanathan</a> and <a href="#vipul">Mr. Vipul Kharbanda</a><br />
<em>This session will present a brief history of the privacy bill(s) in India and end with reflections on ‘habeas data’ as a lens for thinking and actualising privacy after big data.</em></p>
<h4>11:00-11:30 Tea and Coffee</h4>
<h4>11:30-12:30 Digital ID, Data Protection, and Exclusion</h4>
<p><a href="#amelia">Ms. Amelia Andersdotter</a> and <a href="#srikanth">Mr. Srikanth Lakshmanan</a><br />
<em>This session will discuss national centralised digital ID systems, often operating at a cross-functional scale, and highlight its implications for discussions on data protection, welfare governance, and exclusion from public and private services.</em></p>
<h4>12:30-13:30 Digital Money and Financial Inclusion</h4>
<p><a href="#anupam">Dr. Anupam Saraph</a> and <a href="#astha">Ms. Astha Kapoor</a><br />
<em>This session will focus on the rise of digital banking and online payments as core instruments of financial inclusion in India, especially in the context of the Jan Dhan Yojana and UPI, and reflect on the concerns around privacy and financial data.</em></p>
<h4>13:30-14:30 Lunch</h4>
<h4>14:30-15:30 Big Data and Mass Surveillance</h4>
<p><a href="#anja">Dr. Anja Kovacs</a> and <a href="#matthew">Mr. Matthew Rice</a><br />
<em>This session will reflect on the rise of mass communication surveillance across the world, and the evolving challenges of regulating il/legal surveillance by government agencies.</em></p>
<h4>15:30-16:15 Privacy is (a) Right</h4>
<p><a href="#apar">Mr. Apar Gupta</a> and <a href="#kritika">Ms. Kritika Bhardwaj</a><br />
<em>This brief session is to share initial ideas and strategies for articulating and actualising a constitutional right to privacy in India.</em></p>
<h4>16:15-16:30 Tea and Coffee</h4>
<h4>16:30-17:30 Round Table</h4>
<p><em>An open discussion session to conclude the workshop.</em></p>
<h3>Speakers</h3>
<h4 id="amber">Mr. Amber Sinha</h4>
<p>Amber works on issues surrounding privacy, big data, and cyber security. He is interested in the impact of emerging technologies like artificial intelligence and learning algorithms on existing legal frameworks, and how they need to evolve in response. Amber studied humanities and law at National Law School of India University, Bangalore.</p>
<p>E-mail: amber at cis-india dot org.</p>
<p>Twitter: <a href="https://twitter.com/ambersinha07">@ambersinha07</a>.</p>
<h4 id="amelia">Ms. Amelia Andersdotter</h4>
<p>Amelia Andersdotter has been a Member of the European Parliament. She works on practical implications of data protection laws and consumer information security in Sweden, and digital rights in the Europe in general. Presently she is residing in Bangalore, where she is a visiting scholar with Centre for Internet and Society. She holds a BSc in Mathematics.</p>
<p>URL: <a href="https://dataskydd.net">https://dataskydd.net</a>.</p>
<p>Twitter: <a href="https://twitter.com/teirdes">@teirdes</a>.</p>
<h4 id="anja">Dr. Anja Kovacs</h4>
<p>Dr. Anja Kovacs directs the Internet Democracy Project in Delhi, India, which works for an Internet that supports free speech, democracy and social justice in India and beyond. Anja’s research and advocacy focuses especially on questions regarding freedom of expression, cybersecurity and the architecture of Internet governance. She has been a member of the of the Investment Committee of the Digital Defenders Partnership and of the Steering Committee of Best Bits, a global network of civil society members. She has also worked as an international consultant on Internet issues, including for the Independent Commission on Multilateralism, the United Nations Development Programme Asia Pacific and the UN Special Rapporteur on Freedom of Expression, Mr. Frank La Rue, as well as having been a Fellow at the Centre for Internet and Society in Bangalore, India.</p>
<p>Internet Democracy Project: <a href="https://internetdemocracy.in/">https://internetdemocracy.in</a>.</p>
<p>Twitter: <a href="https://twitter.com/anjakovacs">@anjakovacs</a>.</p>
<h4 id="anupam">Dr. Anupam Saraph</h4>
<p>Anupam Saraph has extensively researched India's UID number that has been widely regarded as the game changer in development programs. It has come to be linked with both public and private databases and become the requirement for access to entitlements, benefits, services and rights. Dr. Saraph, who has the design of at least two identification programs to his credit has researched the UID’s functional creep since its inception.</p>
<p>He has been dissecting the myths of what the UID is or is not. He has also tracked the consequences of its linkages on databases that protect national security, sovereignty, democratic status and the entire banking and money system in India. He has also highlighted the implications of its use for targeted delivery of cash subsidies from the Consolidated Fund of India. He has written and lectured widely about the devastating impact of the UID number on development programs, national security and the governability of India.</p>
<p>As a Professor of Systems, Governance and Decision Sciences, Environmental Systems and Business he mentors students and teaches systems, information systems, environmental systems and sustainable development at universities in Europe, Asia and the Americas. He has worked with the Rensselaer Polytechnic Institute, Rijksuniversitiet Groningen, RIVM, University of Edinburgh, Resource Use Institute, Systems Research Institute among others. Dr. Saraph has had the unique distinction of being India’s only person who has held the only office of a City CIO in India, in a PPP arrangement with government, industry and himself. He has also been the first e-governance Advisor to a State government. Dr. Saraph has held CxO and ministerial level positions and serves as an independent director on the boards of Public and Private Sector companies and NGOs. He is also the President of the Nagrik Chetna Manch, an NGO charged with the mission to bring accountability in governance.</p>
<p>Dr. Saraph is also actively engaged in civil society where he participates in several environmental, resource and nature conservation initiatives, has authored draft legislations for river and natural resource conservation, right to good governance and has contributed to governance, election and democratic reforms. Dr. Saraph is a regular columnist in newspapers and writes on issues of governance, future design, technology and education from a systems perspective.</p>
<p>Dr. Saraph is also actively engaged in civil society where he participates in several environmental, resource and nature conservation initiatives, has authored draft legislations for river and natural resource conservation, right to good governance and has contributed to governance, election and democratic reforms. Dr. Saraph is a regular columnist in newspapers and writes on issues of governance, future design, technology and education from a systems perspective.</p>
<p>Dr. Saraph is also actively engaged in civil society where he participates in several environmental, resource and nature conservation initiatives, has authored draft legislations for river and natural resource conservation, right to good governance and has contributed to governance, election and democratic reforms. Dr. Saraph is a regular columnist in newspapers and writes on issues of governance, future design, technology and education from a systems perspective.</p>
<p>As a future designer and recognized as a global expert on complex systems he helps individuals and organisations understand and design the future of their worlds. Together they address the toughest challenges, accomplish missions and achieve business goals. He also supports building capacity to address the challenges of today as well as to build future designs through teams and effective leadership. Since the eighties Dr. Saraph has modeled complex systems of cities, countries, regions and even the planet. His models have been awarded internationally and even placed in 10-year permanent exhibitions.</p>
<p>Dr Saraph works with business and government executives, civil society leaders, politicians, generals, civil servants, police, trade unionists, community activists, United Nations and ASEAN officials, judges, writers, media, architects, designers, technologists, scientists, entrepreneurs, board members and business leaders of small, mid and large single and trans-national companies, religious leaders and artists across a dozen countries and various industry sectors to help them and their organisations succeed in their missions. He advises the World Economic Forum through its Global Agenda Council for Complex Systems and the Club of Rome, Indian National Association as a founder life member.</p>
<p>Dr Saraph holds a PhD in designing sustainable systems from the faculty of Mathematics and Natural Sciences of the Rijksuniversiteit Groningen, the Netherlands.</p>
<p>Website: <a href="http://anupam.saraph.in/">http://anupam.saraph.in</a>.</p>
<p>Twitter: <a href="https://twitter.com/anupamsaraph">@anupamsaraph</a>.</p>
<h4 id="apar">Mr. Apar Gupta</h4>
<p>Apar Gupta practices law in Delhi. He is also one of the co-founders of the Internet Freedom Foundation. His work and writing on public interest issues can be accessed at his personal website <a href="http://www.apargupta.com/">www.apargupta.com</a>.</p>
<p>Twitter: <a href="https://twitter.com/aparatbar">@aparatbar</a>.</p>
<h4 id="astha">Ms. Astha Kapoor</h4>
<p>Astha Kapoor is a public policy strategy consultant working on financial inclusion and digital payments. Currently, she is working with MicroSave. Her tasks involve a focus on government to people (G2P) payments - and her work spans strategy, advisory and evaluation with the DBT Mission, Office of the Chief Economic Advisor, NITI Aayog and ministries pertaining to food, fuel and fertilizer. She recently designed a pilot to digitize uptake of fertilizers in Krishna district, and evaluated the newly introduced coupon system in the Public Distribution System in Bengaluru.</p>
<p>Twitter: <a href="https://twitter.com/kapoorastha">@kapoorastha</a>.</p>
<h4 id="kritika">Ms. Kritika Bhardwaj</h4>
<p>Kritika Bhardwaj works as a Programme Officer at the Centre for Communication Governance (CCG), National Law University, Delhi. Her main areas of research are privacy and data protection. At CCG, she has written about the privacy implications of several contemporary issues such as Aadhaar (India's unique identification project), cloud computing and the right to be forgotten. A lawyer by training, Kritika has a keen interest in information law and human rights law.</p>
<p>Centre for Communication Governance, NLU Delhi: <a href="http://ccgdelhi.org/">http://ccgdelhi.org</a>.</p>
<p>Twitter: <a href="https://twitter.com/Kritika12">@Kritika12</a>.</p>
<h4 id="matthew">Mr. Matthew Rice</h4>
<p>Matthew Rice is an Advocacy Officer at Privacy International working across the organisation engaging with international partners and strengthening their capacity on communications surveillance issues. He has previously worked at Privacy International as a consultant building the Surveillance Industry Index, the largest publicly available database on the private surveillance sector ever assembled. Matthew graduated from University of Aberdeen with an LLB (Hons.) and also has an MA in Human Rights from University College London.</p>
<p>Privacy International: <a href="https://privacyinternational.org/">https://privacyinternational.org</a>.</p>
<p>Twitter: <a href="https://twitter.com/mattr3">@mattr3</a>.</p>
<h4 id="sandeep">Mr. Sandeep Mertia</h4>
<p>Sandeep Mertia is a Research Associate at The Sarai Programme, Centre for the Study of Developing Societies, Delhi. He is an ICT engineer by training with research interests in Science & Technology Studies, Software Studies
and Anthropology. He is conducting an ethnographic study of emerging modes of data-driven knowledge production in the social sector.</p>
<p>Sarai: <a href="http://sarai.net/">http://sarai.net</a>.</p>
<p>Twitter: <a href="https://twitter.com/SandeepMertia">@SandeepMertia</a>.</p>
<p>Academia: <a href="https://daiict.academia.edu/SandeepMertia">https://daiict.academia.edu/SandeepMertia</a>.</p>
<h4 id="srikanth">Mr. Srikanth Lakshmanan</h4>
<p>Srikanth is a software professional with interests in Internet, follower of Internet policy discussions, volunteers for multiple online campaigns related to Internet. He is also fascinated by FOSS, opendata, localization,
Wikipedia, maps, public transit, civic tech and occasionally contributes to them.</p>
<p>Site: <a href="http://www.srik.me/">http://www.srik.me</a>.</p>
<p>Twitter: <a href="https://twitter.com/logic">@logic</a>.</p>
<h4 id="vipul">Mr. Vipul Kharbanda</h4>
<p>Vipul Kharbanda is a consultant with the Center for Internet and Society, Bangalore. After finishing his BA.LLB.(Hons.) from National Law School of India University in Bangalore, he worked for India’s largest corporate law firm for two and a half years in their Mumbai office for two years working primarily on the financing of various infrastructure projects such as Power Plants, Roads, Airports, etc. Since quitting his corporate law job, Vipul has been working as the Associate Editor in a legal publishing house which has been publishing legal books and journals for the last 90 years in India. He has also been involved with the Center for Internet and Society as a Consultant working primarily on issues related to privacy and surveillance.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/events/privacy-after-big-data-delhi-nov-12-2016'>http://editors.cis-india.org/internet-governance/events/privacy-after-big-data-delhi-nov-12-2016</a>
</p>
No publishersumandroData SystemsDigital GovernancePrivacyData RevolutionSurveillanceBig DataDigital IndiaInternet GovernanceBig Data for DevelopmentDigital Rights2016-11-12T10:14:52ZEventRight to Food Campaign, Ranchi Convention, 2016
http://editors.cis-india.org/internet-governance/news/right-to-food-campaign-ranchi-convention-2016
<b>The Right to Food Campaign held its 2016 Convention in Ranchi during September 23-25, 2016. While three years have elapsed since the passage of the National Food Security Act, despite improvements in the Public Distribution System (PDS), large implementation gaps remain. This is what the Convention focused on, and gathered researchers and campaigners from across the country to share experiences and case studies on effectiveness and exclusions from the PDS. Sumandro Chattapadhyay took part in a session of the Convention to discuss how UID-linked welfare delivery is being rolled out across key programmes like provision of pension and rationed distribution of essential commodities, and their impact on people's right to welfare services.</b>
<p> </p>
<h4>Right to Food Campaign: <a href="http://www.righttofoodcampaign.in/">Website</a>.</h4>
<h4>Right to Food Campaign: <a href="https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxoYXFyb3ppcm90aXxneDo3MmQ3MTMyZjU2N2FjOGU">Cash Transfers and UID: Our Main Demands</a>.</h4>
<h4>Ranchi Convention, 2016: <a href="https://docs.google.com/document/d/110_asJ1t14IWALbhWN1RjDiOV8WE-fIK2xJC5Yltyc4/edit">Programme</a>.</h4>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/right-to-food-campaign-ranchi-convention-2016'>http://editors.cis-india.org/internet-governance/news/right-to-food-campaign-ranchi-convention-2016</a>
</p>
No publishersumandroBig DataData SystemsInternet GovernanceSurveillanceAadhaarWelfare GovernanceBiometricsBig Data for DevelopmentUID2019-03-16T04:40:52ZBlog EntryThe Future of Privacy in the Age of Big Data
http://editors.cis-india.org/internet-governance/news/study-tour-on-future-of-privacy-in-age-of-big-data
<b>A study tour on privacy and big data was organised by Friedrich Naumann Foundation for Freedom from September 3 to 10, 2016 in Berlin and Hamburg. Vanya Rakesh was one of the participants from South Asia who went for the tour.</b>
<h3>List of Participants</h3>
<ul>
<li>Shahid Ahmad, Deputy Director, Digital Empowerment Foundation</li>
<li>Shahzad Ahmad, Country Director, Bytes for All</li>
<li>Shivam Satnani, Senior Analyst, Data Security Council of India</li>
<li>Vanya Rakesh, Senior Policy Officer, Centre for Internet & Society</li>
<li>Anja Kovacs, Director, Internet Democracy Project</li>
<li>Tshering Cigay Dorji, CEO, Thimphu Tech Park</li>
<li>Vrinda Bhandari, Lawyer and Journalist, Chambers of Trideep Pais (Anwaltskanzlei)</li>
<li>Tahsin Ifnoor Sayeed, Head of Business Intelligence, DNet</li>
</ul>
<p><a class="external-link" href="http://cis-india.org/internet-governance/files/study-tour-big-data-privacy.pdf">Click to see the Agenda</a></p>
<ul>
</ul>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/news/study-tour-on-future-of-privacy-in-age-of-big-data'>http://editors.cis-india.org/internet-governance/news/study-tour-on-future-of-privacy-in-age-of-big-data</a>
</p>
No publisherpraskrishnaInternet GovernanceBig DataPrivacy2016-09-22T23:24:16ZNews ItemWorkshop on Big Data in India: Benefits, Harms, and Human Rights (Delhi, October 01)
http://editors.cis-india.org/internet-governance/events/big-data-in-india-benefits-harms-and-human-rights-oct-01-2016
<b>CIS welcomes you to participate in the workshop we are organising on Saturday, October 01 at India Habitat Centre, Delhi, to discuss benefits, harms, and human rights implications of big data technologies, and explore potential research questions. A quick RSVP will be much appreciated.</b>
<p> </p>
<h4>Workshop invitation: <a href="http://cis-india.org/internet-governance/files/big-data-in-india-invitatation-to-workshop/at_download/file">Download</a> (PDF)</h4>
<h4>Workshop agenda: <a href="http://cis-india.org/internet-governance/files/big-data-in-india-workshop-agenda/at_download/file">Download</a> (PDF)</h4>
<hr />
<p>In the last few years, there has been an emergence of the discourse of big data viewing it as an instrument not just for ensuring efficient, targeted and personalised services in the private sector, but also for development, social and policy research, and formalising and monetising various sections of the economy. This possibility is premised upon the idea that there is great knowledge that resides in both traditional and new forms of data made possible by our digital selves, and that we may now have the capability to tap into that knowledge for insights across diverse sectors like healthcare, finance, e-governance, education, law enforcement and disaster management, to name but a few. Alongside, various commentators have also pointed to the new problems and risks that big data could create for privacy of individuals through greater profiling, for free speech and economic choice by strengthening monopolistic tendencies, and for socio-economic inequalities by making existing disparities more acute and facilitating algorithmic bias and exclusion.</p>
<p>From a regulatory perspective, big data technologies pose fundamental challenges to the national data regulatory frameworks that have existed since many years. The nature of collection and utilisation of big data, which is often not driven by immediate purpose of the collected data, conflict with the principles of data minimisation and collection limitation that have been integral to data protection laws globally. This compels us to revisit existing theories of data governance. Additionally, use of big data in public decision-making highlights the question of how algorithmic control and governance must be regulated. This raises concerns around taking determining a balanced position that recognises the importance of big data, including for development actions, and ensures unhindered innovation with simultaneous focus on greater transparency and anonymisation to protect individual privacy, and various big data risks faced by population groups. In order to answer these questions, we need to begin with identifying the different harms and benefits of big data that could arise through its use across sectors and disciplines, especially in the context of human rights.</p>
<p>This workshop is designed around an extensive study of current and potential future uses of big data for governance in India that CIS has undertaken over the last year. The study focused on key central government projects and initiatives like the UID project, the Digital India programme, the Smart Cities Challenge, etc.</p>
<p>We will initiate the workshop with a detailed presentation of our findings and key concerns, which will then shape the discussion agenda of the workshop. We look forward to discuss aspects of big data technologies through the entry points of harms, opportunities, and human rights.</p>
<p>The final session of the workshop will focus on identifying key research questions on the topic, and exploring potential alliances of scholars and organisations that can drive such research activities.</p>
<p>We look forward to making this a forum for knowledge exchange for our friends and colleagues attending the discussion and discuss the opportunity to for potential collaboration.</p>
<p><strong>RSVP:</strong> Please send an email to Ajoy Kumar at <<a href="mailto:ajoy@cis-india.org">ajoy@cis-india.org</a>>.</p>
<p><strong>Organisers:</strong> Amber Sinha <<a href="mailto:amber@cis-india.org">amber@cis-india.org</a>> and Sumandro Chattapadhyay <<a href="mailto:sumandro@cis-india.org">sumandro@cis-india.org</a>>.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/events/big-data-in-india-benefits-harms-and-human-rights-oct-01-2016'>http://editors.cis-india.org/internet-governance/events/big-data-in-india-benefits-harms-and-human-rights-oct-01-2016</a>
</p>
No publishervanyaDevelopmentBig DataInternet GovernanceDigital SecurityDigital IndiaDigitisationDigital subjectivitiesBiometricsBig Data for DevelopmentE-GovernanceDigital Rights2016-09-28T05:53:55ZEventReport on Understanding Aadhaar and its New Challenges
http://editors.cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges
<b>The Trans-disciplinary Research Cluster on Sustainability Studies at Jawaharlal Nehru University collaborated with the Centre for Internet and Society, and other individuals and organisations to organise a two day workshop on “Understanding Aadhaar and its New Challenges” at the Centre for Studies in Science Policy, JNU on May 26 and 27, 2016. The objective of the workshop was to bring together experts from various fields, who have been rigorously following the developments in the Unique Identification (UID) Project and align their perspectives and develop a shared understanding of the status of the UID Project and its impact. Through this exercise, it was also sought to develop a plan of action to address the welfare exclusion issues that have arisen due to implementation of the UID Project.</b>
<p> </p>
<h4>Report: <a href="http://editors.cis-india.org/internet-governance/files/report-on-understanding-aadhaar-and-its-new-challenges/at_download/file">Download</a> (PDF)</h4>
<hr />
<p style="text-align: justify;">This Report is a compilation of the observations made by participants at the workshop relating to myriad issues under the UID Project and various strategies that could be pursued to address these issues. In this Report we have classified the observations and discussions into following themes:</p>
<p><strong>1.</strong> <a href="#1">Brief Background of the UID Project</a></p>
<p><strong>2.</strong> <a href="#2">Legal Status of the UIDAI Project</a></p>
<ul>
<li><a href="#21">Procedural issues with passage of the Act</a></li>
<li><a href="#22">Status of related litigation</a></li></ul>
<p><strong>3.</strong> <a href="#3">National Identity Projects in Other Jurisdictions</a></p>
<ul>
<li><a href="#31">Pakistan</a></li>
<li><a href="#32">United Kingdom</a></li>
<li><a href="#33">Estonia</a></li>
<li><a href="#34">France</a></li>
<li><a href="#35">Argentina</a></li></ul>
<p><strong>4.</strong> <a href="#4">Technologies of Identification and Authentication</a></p>
<ul>
<li><a href="#41">Use of Biometric Information for Identification and Authentication</a></li>
<li><a href="#42">Architectures of Identification</a></li>
<li><a href="#43">Security Infrastructure of CIDR</a></li></ul>
<p><strong>5.</strong> <a href="#5">Aadhaar for Welfare?</a></p>
<ul>
<li><a href="#51">Social Welfare: Modes of Access and Exclusion</a></li>
<li><a href="#52">Financial Inclusion and Direct Benefits Transfer</a></li></ul>
<p><strong>6.</strong> <a href="#6">Surveillance and UIDAI</a></p>
<p><strong>7.</strong> <a href="#7">Strategies for Future Action</a></p>
<p><strong>Annexure A</strong> <a href="#AA">Workshop Agenda</a></p>
<p><strong>Annexure B</strong> <a href="#AB">Workshop Participants</a></p>
<hr />
<h3 id="1" style="text-align: justify;"><strong>1. Brief Background of the UID Project</strong></h3>
<p style="text-align: justify;">In the year 2009, the UIDAI was established and the UID project was conceived by the Planning Commission under the UPA government to provide unique identification for each resident in India and to be used for delivery of welfare government services in an efficient and transparent manner, along with using it as a tool to monitor government schemes. The objective of the scheme has been to issue a unique identification number by the Unique Identification Authority of India, which can be authenticated and verified online. It was conceptualized and implemented as a platform to facilitate identification and avoid fake identity issues and delivery of government benefits based on the demographic and biometric data available with the Authority.</p>
<p style="text-align: justify;">The Aadhaar (Targeted Delivery of Financial and Other Subsidies, Benefits and Services) Act, 2016 (the “<strong>Act</strong>”) was passed as a money bill on March 16, 2016 and was notified in the gazette March 25, 2016 upon receiving the assent of the President. However, the enforceability date has not been mentioned due to which the bill has not come into force.</p>
<p style="text-align: justify;">The Act provides that the Aadhaar number can be used to validate a person’s identity, but it cannot be used as a proof of citizenship. Also, the government can make it mandatory for a person to authenticate her/his identity using Aadhaar number before receiving any government subsidy, benefit, or service. At the time of enrolment, the enrolling agency is required to provide notice to the individual regarding how the information will be used, the type of entities the information will be shared with and their right to access their information. Consent of an individual would be obtained for using his/her identity information during enrolment as well as authentication, and would be informed of the nature of information that may be shared. The Act clearly lays that the identity information of a resident shall not be sued for any purpose other than specified at the time of authentication and disclosure of information can be made only pursuant to an order of a court not inferior to that of a District Judge and/or disclosure made in the interest of national security.</p>
<h3 id="2" style="text-align: justify;"><strong>2. Legal Status of the UIDAI Project</strong></h3>
<p style="text-align: justify;">In this section, we have summarised the discussions on the procedural issues with the passage of the Act. The participants had criticised the passage of the Act as a money bill in the Parliament. The participants also assessed the litigation pending in the Supreme Court of India that would be affected by this law. These discussions took place in the session titled, ‘Current Status of Aadhaar’ and have been summarised below.</p>
<h3 id="21" style="text-align: justify;">Procedural Issues with Passage of the Act</h3>
<p style="text-align: justify;">The participants contested the introduction of the Act in the form of a money bill. The rationale behind this was explained at the session and is briefly explained here. Article 110 (1) of the Constitution of India defines a money bill as one containing provisions only regarding the matters enumerated or any matters incidental to the following: a) imposition, regulation and abolition of any tax, b) borrowing or other financial obligations of the Government of India, c) custody, withdrawal from or payment into the Consolidated Fund of India (CFI) or Contingent Fund of India, d) appropriation of money out of CFI, e) expenditure charged on the CFI or f) receipt or custody or audit of money into CFI or public account of India. The Act makes references to benefits, subsidies and services which are funded by the Consolidated Fund of India (CFI), however the main objectives of the Act is to create a right to obtain a unique identification number and provide for a statutory mechanism to regulate this process. The Act only establishes an identification mechanism which facilitates distribution of benefits and subsidies funded by the CFI and this identification mechanism (Aadhaar number) does not give it the character of a money bill. Further, money bills can be introduced only in the Lok Sabha, and the Rajya Sabha cannot make amendments to such bills passed by the Lok Sabha. The Rajya Sabha can suggest amendments, but it is the Lok Sabha’s choice to accept or reject them. This leaves the Rajya Sabha with no effective role to play in the passage of the bill.</p>
<p style="text-align: justify;">The participants also briefly examined the writ petition that has been filed by former Union minister Jairam Ramesh challenging the constitutionality and legality of the treatment of this Act as a money bill which has raised the question of judiciary’s power to review the decisions of the speaker. Article 122 of the Constitution of India provides that this power of judicial review can be exercised to look into procedural irregularities. The question remains whether the Supreme Court will rule that it can determine the constitutionality of the decision made by the speaker relating to the manner in which the Act was introduced in the Lok Sabha. A few participants mentioned that similar circumstances had arisen in the case of Mohd. Saeed Siddiqui v. State of U.P. <a href="#ftn1">[1]</a>.</p>
<p style="text-align: justify;">where the Supreme Court refused to interfere with the decision of the Uttar Pradesh legislative assembly speaker certifying an amendment bill to increase the tenure of the Lokayukta as a money bill, despite the fact that the bill amended the Uttar Pradesh Lokayukta and Up-Lokayuktas Act, 1975, which was passed as an ordinary bill by both houses. The Court in this case held that the decision of the speaker was final and that the proceedings of the legislature being important legislative privilege could not be inquired into by courts. The Court added, “the question whether a bill is a money bill or not can be raised only in the state legislative assembly by a member thereof when the bill is pending in the state legislature and before it becomes an Act.”</p>
<p style="text-align: justify;">However, it is necessary to carve a distinction between Rajya Sabha and State Legislature. Unlike the State Legislature, constitution of Rajya Sabha is not optional therefore significance of the two bodies in the parliamentary process cannot be considered the same. Participants also made another significant observation about a similar bill on the UID project (National Identification Authority of India (NIDAI) Bill) that was introduced before by the UPA government in 2010 and was deemed unacceptable by the standing committee on finance, headed by Yashwant Sinha. This bill was subsequently withdrawn.</p>
<h3 id="22" style="text-align: justify;">Status of Related Litigation</h3>
<p style="text-align: justify;">A panellist in this session briefly summarised all the litigation that was related to or would be affected by the Act. The panellist also highlighted several Supreme Court orders in the case of <em>KS Puttuswamy v. Union of India</em> <a href="#ftn2">[2]</a> which limited the use of Aadhaar. We have reproduced the presentation below.</p>
<ul>
<li style="text-align: justify;"><em>KS Puttuswamy v. Union of India</em> - This petition was filed in 2012 with primary concern about providing Aadhaar numbers to illegal immigrants in India. It was contended that this could not be done without a law establishing the UIDAI and amendment to the Citizenship laws. The petitioner raised concerns about privacy and fallibility of biometrics.</li>
<li style="text-align: justify;"> Sudhir Vombatkere & Bezwada Wilson <a href="#ftn3">[3]</a> - This petition was filed in 2013 on grounds of infringement of right to privacy guaranteed under Article 21 of the Constitution of India and the security threat on account of data convergence.</li>
<li style="text-align: justify;">Aruna Roy & Nikhil Dey <a href="#ftn4">[4]</a> - This petition was filed in 2013 on the grounds of large scale exclusion of people from access to basic welfare services caused by UID. After their petition, no. of intervention applications were filed. These were the following:</li>
<li style="text-align: justify;">Col. Mathew Thomas <a href="#ftn5">[5]</a> - This petition was filed on the grounds of threat to national security posed by the UID project particularly in relation to arrangements for data sharing with foreign companies (with links to foreign intelligence agencies).</li>
<li style="text-align: justify;">Nagrik Chetna Manch <a href="#ftn6">[6]</a> - This petition was filed in 2013 and led by Dr. Anupam Saraph on the grounds that the UID project was detrimental to financial service regulation and financial <em>inclusion.</em></li>
<li style="text-align: justify;">S. Raju <a href="#ftn7">[7] </a> - This petition was filed on the grounds that the UID project had implications on the federal structure of the State and was detrimental to financial inclusion.</li>
<li style="text-align: justify;"><em>Beghar Foundation</em> - This petition was filed in 2013 in the Delhi High Court on the grounds invasion of privacy and exclusion specifically in relation to the homeless. It subsequently joined the petition filed by Aruna Roy and Nikhil Dey as an intervener.</li>
<li style="text-align: justify;">Vickram Crishna – This petition was originally filed in the Bombay High Court in 2013 on the grounds of surveillance and invasion of privacy. It was later transferred to the Supreme Court.</li>
<li style="text-align: justify;">Somasekhar – This petition was filed on the grounds of procedural unreasonableness of the UID project and also exclusion & privacy. The petitioner later intervened in the petition filed by Aruna Roy and Nikhil Dey in 2013.</li>
<li style="text-align: justify;">Rajeev Chandrashekhar– This petition was filed on the ground of lack of legal sanction for the UID project. He later intervened in the petition filed by Aruna Roy and Nikhil Dey in 2013. His position has changed now.</li>
<li style="text-align: justify;">Further, a petition was filed by Mr. Jairam Ramesh initially challenging the passage of the Act as a money bill but subsequently, it has been amended to include issues of violation of right to privacy and exclusion of the poor and has advocated for five amendments that were suggested to the Aadhaar Bill by the Rajya Sabha.</li></ul>
<h3 id="23" style="text-align: justify;">Relevant Orders of the Supreme Court</h3>
<p>There are six orders of the Supreme Court which are noteworthy.</p>
<ul>
<li style="text-align: justify;">Order of Sept. 23, 2013 - The Supreme court directed that: 1) no person shall suffer for not having an aadhaar number despite the fact that a circular by an authority makes it mandatory; 2) it should be checked if a person applying for aadhaar number voluntarily is entitled to it under the law; and 3) precaution should be taken that it is not be issued to illegal immigrants.</li>
<li style="text-align: justify;">Order of 26th November, 2013 – Applications were filed by UIDAI, Ministry of Petroleum & Natural Gas, Govt of India, Indian Oil Corporation, BPCL and HPCL for modifying the September 23rd order and sought permission from the Supreme Court to make aadhaar number mandatory. The Supreme Court held that the order of September 23rd would continue to be effective.</li>
<li style="text-align: justify;">Order of 24th March, 2014 – This order was passed by the Supreme Court in a special leave petition filed in the case of <em>UIDAI v CBI</em> <a href="#ftn8">[8] </a> wherein UIDAI was asked to UIDAI to share biometric information of all residents of a particular place in Goa to facilitate a criminal investigation involving charges of rape and sexual assault. The Supreme Court restrained UIDAI from transferring any biometric information of an individual without to any other agency without his consent in writing. The Supreme Court also directed all the authorities to modify their forms/circulars/likes so as to not make aadhaar number mandatory.</li>
<li style="text-align: justify;">Order of 16th March, 2015 - The SC took notice of widespread violations of the order passed on September 23rd, 2013 and directed the Centre and the states to adhere to these orders to not make aadhaar compulsory.</li>
<li style="text-align: justify;">Orders of August 11, 2015 – In the first order, the Central Government was directed to publicise the fact that aadhaar was voluntary. The Supreme Court further held that provision of benefits due to a citizen of India would not be made conditional upon obtaining an aadhaar number and restricted the use of aadhaar to the PDS Scheme and in particular for the purpose of distribution of foodgrains, etc. and cooking fuel, such as kerosene and the LPG Distribution Scheme. The Supreme Court also held that information of an individual that was collected in order to issue an aadhaar number would not be used for any purpose except when directed by the Court for criminal investigations. Separately, the status of fundamental right to privacy was contested and accordingly the Supreme Court directed that the issue be taken up before the Chief Justice of India.</li>
<li style="text-align: justify;">Orders of October 16, 2015 – The Union of India, the states of Gujarat, Maharashtra, Himachal Pradesh and Rajasthan, and authorities including SEBI, TRAI, CBDT, IRDA , RBI applied for a hearing before the Constitution Bench for modification of the order passed by the Supreme Court on August 11 and allow use of aadhaar number schemes like The Mahatma Gandhi National Rural Employment Guarantee Scheme MGNREGS), National Social Assistance Programme (Old Age Pensions, Widow Pensions, Disability Pensions) Prime Minister's Jan Dhan Yojana (PMJDY) and Employees' Providend Fund Organisation (EPFO). The Bench allowed the use of aadhaar number for these schemes but stressed upon the need to keep aadhaar scheme voluntary until the matter was finally decided.</li></ul>
<p style="text-align: justify;">Status of these orders<br />The participants discussed the possible impact of the law on the operation of these orders. A participant pointed out that matters in the Supreme Court had not become infructuous because fundamental issues that were being heard in the Supreme Court had not been resolved by the passage of the Act. Several participants believed that the aforementioned orders were effective because the law had not come into force. Therefore, aadhaar number could only be used for purposes specified by the Supreme Court and it could not be made mandatory. Participants also highlighted that when the Act was implemented, it would not nullify the orders of the Supreme Court unless Union of India asked the Supreme Court for it specifically and the Supreme Court sanctioned that.</p>
<h3 id="3" style="text-align: justify;"><strong>3. National Identity Projects in Other Jurisdictions</strong></h3>
<p style="text-align: justify;">A panellist had provided a brief overview of similar programs on identification that have been launched in other jurisdictions including Pakistan, United Kingdom, France, Estonia and Argentina in the recent past in the session titled ‘Aadhaar - International Dimensions’. This presentation mainly sought to assess the incentives that drove the governments in these jurisdictions to formulate these projects, mandatory nature of their adoption and their popularity. The Report has reproduced the presentation here.</p>
<h3 id="31" style="text-align: justify;">Pakistan</h3>
<p style="text-align: justify;">The Second Amendment to the Constitution of Pakistan in 2000 established the National Database and Regulation Authority in the country, which regulates government databases and statistically manages the sensitive registration database of the citizens of Pakistan. It is also responsible for issuing national identity cards to the citizens of Pakistan. Although the card is not legally compulsory for a Pakistani citizen, it is mandatory for:</p>
<ul>
<li>Voting</li>
<li>Obtaining a passport</li>
<li>Purchasing vehicles and land</li>
<li>Obtaining a driver licence</li>
<li>Purchasing a plane or train ticket</li>
<li>Obtaining a mobile phone SIM card</li>
<li>Obtaining electricity, gas, and water</li>
<li>Securing admission to college and other post-graduate institutes</li>
<li>Conducting major financial transactions</li></ul>
<p style="text-align: justify;">Therefore, it is pretty much necessary for basic civic life in the country. In 2012, NADRA introduced the Smart National Identity Card, an electronic identity card, which implements 36 security features. The following information can be found on the card and subsequently the central database: Legal Name, Gender (male, female, or transgender), Father's name (Husband's name for married females), Identification Mark, Date of Birth, National Identity Card Number, Family Tree ID Number, Current Address, Permanent Address, Date of Issue, Date of Expiry, Signature, Photo, and Fingerprint (Thumbprint). NADRA also records the applicant's religion, but this is not noted on the card itself. (This system has not been removed yet and is still operational in Pakistan.)</p>
<h3 id="32" style="text-align: justify;">United Kingdom</h3>
<p style="text-align: justify;">The Identity Cards Act was introduced in the wake of the terrorist attacks on 11th September, 2001, amidst rising concerns about identity theft and the misuse of public services. The card was to be used to obtain social security services, but the ability to properly identify a person to their true identity was central to the proposal, with wider implications for prevention of crime and terrorism. The cards were linked to a central database (the National Identity Register), which would store information about all of the holders of the cards. The concerns raised by human rights lawyers, activists, security professionals and IT experts, as well as politicians were not to do with the cards as much as with the NIR. The Act specified 50 categories of information that the NIR could hold, including up to 10 fingerprints, digitised facial scan and iris scan, current and past UK and overseas places of residence of all residents of the UK throughout their lives. The central database was purported to be a prime target for cyber attacks, and was also said to be a violation of the right to privacy of UK citizens. The Act was passed by the Labour Government in 2006, and repealed by the Conservative-Liberal Democrat Coalition Government as part of their measures to “reverse the substantial erosion of civil liberties under the Labour Government and roll back state intrusion.”</p>
<h3 id="33" style="text-align: justify;">Estonia</h3>
<p style="text-align: justify;">The Estonian i-card is a smart card issued to Estonian citizens by the Police and Border Guard Board. All Estonian citizens and permanent residents are legally obliged to possess this card from the age of 15. The card stores data such as the user's full name, gender, national identification number, and cryptographic keys and public key certificates. The cryptographic signature in the card is legally equivalent to a manual signature, since 15 December 2000. The following are a few examples of what the card is used for:</p>
<ul>
<li>As a national ID card for legal travel within the EU for Estonian citizens</li>
<li>As the national health insurance card</li>
<li>As proof of identification when logging into bank accounts from a home computer</li>
<li>For digital signatures</li>
<li>For i-voting</li>
<li>For accessing government databases to check one’s medical records, file taxes, etc.</li>
<li>For picking up e-Prescriptions</li>
<li>(This system is also operational in the country and has not been removed)</li></ul>
<h3 id="34" style="text-align: justify;">France</h3>
<p style="text-align: justify;">The biometric ID card was to include a compulsory chip containing personal information, such as fingerprints, a photograph, home address, height, and eye colour. A second, optional chip was to be implemented for online authentication and electronic signatures, to be used for e-government services and e-commerce. The law was passed with the purpose of combating “identity fraud”. It was referred to the Constitutional Council by more than 200 members of the French Parliament, who challenged the compatibility of the bill with the citizens’ fundamental rights, including the right to privacy and the presumption of innocence. The Council struck down the law, citing the issue of proportionality. “Regarding the nature of the recorded data, the range of the treatment, the technical characteristics and conditions of the consultation, the provisions of article 5 touch the right to privacy in a way that cannot be considered as proportional to the meant purpose”.</p>
<h3 id="35" style="text-align: justify;">Argentina</h3>
<p style="text-align: justify;">Documento Nacional de Identidad or DNI (which means National Identity Document) is the main identity document for Argentine citizens, as well as temporary or permanent resident aliens. It is issued at a person's birth, and updated at 8 and 14 years of age simultaneously in one format: a card (DNI tarjeta); it's valid if identification is required, and is required for voting. The front side of the card states the name, sex, nationality, specimen issue, date of birth, date of issue, date of expiry, and transaction number along with the DNI number and portrait and signature of the card's bearer. The back side of the card shows the address of the card's bearer along with their right thumb fingerprint. The front side of the DNI also shows a barcode while the back shows machine-readable information. The DNI is a valid travel document for entering Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, Uruguay, and Venezuela. (System still operational in the country)</p>
<h3 id="4" style="text-align: justify;"><strong>4. Technologies of Identification and Authentication</strong></h3>
<p style="text-align: justify;">The panel in the session titled ‘Aadhaar: Science, Technology, and Security’ explained the technical aspects of use of biometrics and privacy concerns, technology architecture for identification and inadequacy of infrastructure for information security. In this section, we have summarised the presentation and the ensuing discussions on these issues.</p>
<h3 id="41" style="text-align: justify;">Use of Biometric Information for Identification and Authentication</h3>
<p style="text-align: justify;">The panelists explained with examples that identification and authentication were different things. Identity provides an answer to the question “who are you?” while authentication is a challenge-response process that provides a proof of the claim of identity. Common examples of identity are User ID (Login ID), cryptographic public keys and ATM or Smart cards while common authenticators are passwords (including OTPs), PINs and cryptographic private keys. Identity is public information but an authenticator must be private and known only to the user. Authentication must necessarily be a conscious process and active participation by the user is a must. It should also always be possible to revoke an authenticator. After providing this understanding of the two processes the panellist then explained if biometric information could be used for identification or authentication under the UID Project. Biometric information is clearly public information and it is questionable if it can be revoked. Therefore it should never be used for authentication, but only for identity verification. There is a possibility of authentication by fingerprints under the UID Project, without conscious participation of the user. One could trace the fingerprints of an individual from any place the individual has been in contact with. Therefore, authentication must certainly be done by other means. The panellist pointed out that there were five kinds of authentication under the UID Project, out of which two-factor authentication and one time password were considered suitable but use of biometric information and demographic information was extremely threatening and must be withdrawn.</p>
<h3 id="42" style="text-align: justify;">Architectures of Identification</h3>
<p style="text-align: justify;">The panelists explained the architecture of the UID Project that has been designed for identification purposes, highlighted its limitations and suggested alternatives. His explanations are reproduced below.</p>
<p style="text-align: justify;">Under the UID Project, there is a centralised means of identification i.e. the aadhaar number and biometric information stored in one place, Central Identification Data Repository (CIDR). It is better to have multiple means of identification than one (as contemplated under the UID Project) for preservation of our civil liberties. The question is what the available alternatives are. Web of trust is a way for operationalizing distributed identification but the challenge is how one brings people from all social levels to participate in it. There is a need for registrars who will sign keys and public databases for this purpose.</p>
<p style="text-align: justify;">The aadhaar number functions as a common index and facilitates correlation of data across Government databases. While this is tremendously attractive it raises several privacy concerns as more and more information relating to an individual is available to others and is likely to be abused.</p>
<p style="text-align: justify;">The aadhaar number is available in human readable form. This raises the risk of identification without consent and unauthorised profiling. It cannot be revoked. Potential for damage in case of identity theft increases manifold.</p>
<p style="text-align: justify;">Under the UID Project, for the purpose of information security, Authentication User Agencies (“<strong>AUA</strong>”) are required to use local identifiers instead of aadhaar numbers but they are also required to map these local identifiers to the aadhaar numbers. Aadhaar numbers are not cryptographically secured; in fact they are publicly available. Hence this exercise for securing information is useless. An alternative would be to issue different identifiers for different domains and cryptographically embed a “master identifier” (in this case, equivalent of aadhaar number) into each local identifier.</p>
<p style="text-align: justify;">All field devices (for example POS machines) should be registered and must communicate directly with UIDAI. In fact, UIDAI must verify the authenticity (tamper proof) of the field device during run time and a UIDAI approved authenticity certificate must be issued for field devices. This certificate must be made available to users on demand. Further, the security and privacy frameworks within which AUAs work must be appropriately defined by legal and technical means.</p>
<h3 id="43" style="text-align: justify;">Security Infrastructure of CIDR</h3>
<p style="text-align: justify;">The panelists also enumerated the security features of the UID Project and highlighted the flaws in these features. These have been summarised below.</p>
<p>The security and privacy infrastructure of UIDAI has the following main features:</p>
<ul>
<li>2048 bit PKI encryption of biometric data in transit</li>
<li>End-to-end encryption from enrolment/POS to CIDR</li>
<li>HMAC based tamper detection of PID blocks</li>
<li>Registration and authentication of AUAs</li>
<li>Within CIDR only a SHA 1 Hash of Aadhaar number is stored</li>
<li>Audit trails are stored SHA 1 encrypted. Tamper detection?</li>
<li>Only hashes of passwords and PINs are stored. (biometric data stored in original form though!)</li>
<li>Authentication requests have unique session keys and HMAC</li>
<li>Resident data stored using 100 way sharding (vertical partitioning). First two digits of Aadhaar number as shard keys</li>
<li>All enrolment and update requests link to partitioned databases using Ref IDs (coded indices)</li>
<li>All accesses through a hardware security module</li>
<li>All analytics carried out on anonymised data</li></ul>
<p style="text-align: justify;">The panellists pointed out the concerns about information security on account of design flaws, lack of procedural safeguards, openness of the system and too much trust imposed on multiple players. All symmetric and private keys and hashes are stored somewhere within UIDAI. This indicates that trust is implicitly assumed which is a glaring design flaw. There is no well-defined approval procedure for data inspection, whether it is for the purpose of investigation or for data analytics. There is a likelihood of system hacks, insider leaks, and tampering of authentication records and audit trails. The ensuing discussions highlighted that the UIDAI had admitted to these security risks. The enrolment agencies and the enrolment devices cannot be trusted. AUAs cannot be trusted with biometric and demographic data; neither can they be trusted with sensitive user data of private nature. There is a need for an independent third party auditor for distributed key management, auditing and approving UIDAI programs, including those for data inspection and analytics, whitebox cryptographic compilation of critical parts of the UIDAI programs, issue of cryptographic keys to UIDAI programs for functional encryption, challenge-response for run-time authentication and certification of UIDAI programs. The panellist recommended that there was a need to to put a suitable legal framework to execute this.</p>
<p style="text-align: justify;">The participants also discussed that information infrastructure must not be made of proprietary software (possibility for backdoors for US) and there must be a third party audit with a non-negotiable clause for public audit.</p>
<h3 id="5" style="text-align: justify;"><strong>5. Aadhaar for Welfare?</strong></h3>
<p style="text-align: justify;">The Report has summarised the discussions that took place in the sessions on ‘Direct Benefits Transfers’ and ‘Aadhaar: Broad Issues - II’ where the panellists critically analysed the claims of benefits and inclusion of Aadhaar made by the government in light of the ground realities in states where Aadhaar has been adopted for social welfare schemes.</p>
<h3 id="51" style="text-align: justify;">Social Welfare: Modes of Access and Exclusion</h3>
<p style="text-align: justify;">Under the Act, a person may be required to authenticate or give proof of the aadhaar number in order to receive subsidy from the government (Section 7). A person is required to punch their fingerprints on POS machines in order to receive their entitlement under the social welfare schemes such as LPG and PDS. It was pointed out in the discussions that various states including Rajasthan and Delhi had witnessed fingerprint errors while doling out benefits at ration shops under the PDS scheme. People have failed to receive their entitled benefits because of these fingerprint errors thus resulting in exclusion of beneficiaries <a href="#ftn9">[9]</a>. A panellist pointed out that in Rajasthan, dysfunctional biometrics had led to further corruption in ration shops. Ration shop owners often lied to the beneficiaries about functioning of the biometric machines (POS Machines) and kept the ration for sale in the market therefore making a lot of money at the expense of uninformed beneficiaries and depriving them of their entitlements.</p>
<p style="text-align: justify;">Another participant organisation also pointed out similar circumstances in the ration shops in Patparganj and New Delhi constituencies. Here, the dealers had maintained the records of beneficiaries who had been categorized as follows: beneficiaries whose biometrics did not match, beneficiaries whose biometrics matched and entitlements were provided, beneficiaries who never visited the ration shop. It had been observed that there were no entries in the category of beneficiaries whose biometrics did not match however, the beneficiaries had a different story to tell. They complained that their biometrics did not match despite trying several times and there was no mechanism for a manual override. Consequently, they had not been able to receive any entitlements for months. The discussions also pointed out that the food authorities had placed complete reliance on authenticity of the POS machines and claim that this system would weed out families who were not entitled to the benefits. The MIS was also running technical glitches as a result there was a problem with registering information about these transactions hence, no records had been created with the State authority about these problems. A participant also discussed the plight of 30,000 widows in Delhi, who were entitled to pension and used to collect their entitlement from post offices, faced exclusion due to transition problems under the Jan Dhan Yojana (after the Jandhan was launched the money was transferred to their bank accounts in order to resolve the problem of misappropriation of money at the hands of post office officials). These widows were asked to open bank accounts to receive their entitlements and those who did not open these accounts and did not inform the post office were considered bogus.</p>
<p style="text-align: justify;">In the discussions, the participants also noted that this unreliability of fingerprints as a means of authentication of an individual’s identity was highlighted at the meeting of Empowered Group of Ministers in 2011 by J Dsouza, a biometrics scientist. He used his wife’s fingerprints to demonstrate that fingerprints may change overtime and in such an event, one would not be able to use the POS machine anymore as the machine would continue to identify the impressions collected initially.</p>
<p style="text-align: justify;">The participants who had been working in the field had contributed to the discussions by busting the myth that the UID Project helped to identify who was poor and resolve the problem of exclusion due to leakages in the social welfare programs. These discussions have been summarised below.</p>
<ul>
<li style="text-align: justify;">It is important to understand that the UID Project is merely an identification and authentication system. It only helps in verifying if an individual is entitled to benefits under a social security scheme. It does not ensure plugging of leakages and reducing corruption in social security schemes as has been claimed by the Government. The reduction in leakage of PDS, for instance, should be attributed to digitization and not UID. The Government claims, that it has saved INR 15000 crore in provision of LPG on identification of 3.34 crore inactive accounts on account of the UID Project. This is untrue because the accounts were weeded by using mechanisms completely unrelated to the UID Project. Consequently, the savings on account of UID are only of INR 120 crore and not 15000 crore.</li>
<li style="text-align: justify;">The UID Project has resulted in exclusion of people either because they do not have an aadhaar number, or they have a wrong identification, or there are errors of classification or wilful misclassification. About 99.7% people who were given aadhaar numbers already had an identification document. In fact, during enrolment a person is required to produce one of 14 identification documents listed under the law in order to get an aadhaar number which makes it very difficult for a person with no identity to become entitled to a social welfare scheme.</li></ul>
<p style="text-align: justify;">A participant condemned the Government’s claim that the UID Project had helped in removing fake, bogus and duplicate cards and said that these terms could not be used synonymously and the authorities had no clarity about the difference between the meanings of these terms. The UID Project had only helped in removal of duplicate cards but had not helped in combating the use of fake and bogus cards.</p>
<h3 id="52" style="text-align: justify;">Financial Inclusion and Direct Benefits Transfer</h3>
<p style="text-align: justify;">The participants also engaged in the discussions about the impact of the UID project on financial inclusion in India in the sessions titled ‘Aadhaar: Broad Issues - I & II’. We have summarised these discussions below.</p>
<p style="text-align: justify;">The UID Project seeks to directly transfer money to a bank account in order to combat corruption. The discussions highlighted that this was nothing but introducing a neo liberal thrust in social policy and that it was not feasible for various reasons. First, 95% of rural India did not have functioning banks and banks are quite far away. Second, in order to combat this dearth of banks the idea of business correspondents, who handled banking transactions and helped in opening of bank accounts, had been introduced which had created various problems. The Reserve Bank of India reported that there was dearth of business correspondents as there was very little incentive to become one; their salary is merely INR 4000. Third, there were concerns about how an aadhaar number was considered a valid document for Know Your Customer (KYC) checks. There was a requirement for scrutiny and auditing of documents submitted during the time of enrolment which, in the present scheme of things, could not be verified. Fourth, there were no restrictions on number of bank accounts that could be opened with a single aadhaar number which gave rise to a possibility of opening multiple and shell accounts on a single aadhaar number. Therefore, records only showed transactions when money was transferred from an aadhaar number to another aadhaar number as opposed to an account-to-account transfer. The discussion relied on NPCI data which shows which bank an aadhaar number is associated with but does not show if a transaction by an aadhaar number is overwritten by another bank account belonging to the same aadhaar number.</p>
<h3 id="6" style="text-align: justify;"><strong>6. Surveillance and UIDAI</strong></h3>
<p style="text-align: justify;">The participants had discussed the possibility of an alternative purpose for enrolling Aadhaar in the session titled ‘Privacy, Surveillance, and Ethical Dimensions of Aadhaar’. The discussion traced the history of this project to gain insight on this issue. We have summarised below the key take aways from this discussion.</p>
<p style="text-align: justify;">There are claims that the main objective of launching the UID Project is not to facilitate implementation of social security schemes but to collect personal (financial and non-financial) information of the citizens and residents of the country to build a data monopoly. For this purpose, PDS was chosen as a suitable social security scheme as it has the largest coverage. Several participants suggested that numerous reports authored by FICCI, KPMG and ASSOCHAM contained proposals for establishing a national identity authority which threw some light on the commercial intentions behind information collection under the UID Project.</p>
<p style="text-align: justify;">It was also pointed out that there was documented proof that information collected under the UID Project might have been shared with foreign companies. There are suggestions about links established between proponents of the UID Project and companies backed by CIA or the French Government which run security projects and deal in data sharing in several jurisdictions.</p>
<h3 id="7" style="text-align: justify;"><strong>7. Strategies for Future Action</strong></h3>
<p>The participants laid down a list of measures that must be taken to take the discussions forward. We have enumerated these recommendations below.</p>
<ul>
<li>Prepare and compile an anthology of articles as an output of this workshop. </li>
<li>Prepare position papers on specific issues related to the UID Project </li>
<li>Prepare pamphlets/brochures on issues with the UID Project for public consumption </li>
<li>Prepare counter-advertisements for Aadhaar</li>
<li>Publish existing empirical evidence on the flaws in Aadhaar.</li>
<li>Set up an online portal dedicated to providing updates on the UID Project and allows discussions on specific issues related to Aadhaar.</li>
<li>Use Social Media to reach out to the public. Regularly track and comment on social media pages of relevant departments of the government.</li>
<li>Create groups dedicated to research and advocacy of specific aspects of the UID Project. </li>
<li>Create a Coordination Committee preferably based in Delhi which would be responsible for regularly holding meetings and for preparing a coordinated plan of action. Employ permanent to staff to run the Committee.</li>
<li>Organise an advocacy campaign against use of Aadhaar in collaboration with other organisations and build public domain acceptance. </li>
<li>The campaign must specifically focus on the unfettered scope of UID and expanse, misrepresentation of the success of Aadhaar by highlighting real savings, technological flaws, status of pilot programs and increasing corruption on account of the UID Project</li>
<li>Prepare a statement of public concern regarding the UID Project and collect signatures from eminent persons including academics, technical experts, civil society groups and members of parliament.</li>
<li>Organise events and discussions on issues relating to Aadhaar and invite members og government departments to speak and discuss the issues. </li>
<li style="text-align: justify;">Write to Members of Parliament and Members of Legislative Assemblies raising questions on their or their parties’ support for Aadhaar and silence on the problems created by the UID Project. </li>
<li style="text-align: justify;">Organise public hearings in states like Rajasthan to observe and document ground realities of the UID Project and share these outcomes with the state government and media. </li>
<li>Plan a national social audit and public hearing on the working of UID Project in the country. </li>
<li style="text-align: justify;">File Contempt Petitions in the Supreme Court and High Courts against mandatory use of Aadhaar number for services not allowed by the Supreme Court. </li>
<li style="text-align: justify;">Reach out to and engage with various foreign citizens and organisations that have been fighting on similar issues. The organisations and individuals who could be approached would include EPIC, Electronic Frontier foundation, David Moss, UK, Roger Clarke, Australia, Prof. Ian Angel, Snowden, Assange and Chomsky.</li>
<li style="text-align: justify;">Work towards increasing awareness about the UID Project and gaining support from the student and research community, student organisations, trade unions, and other associations and networks in the unorganised sector.</li></ul>
<h3 id="AA" style="text-align: justify;"><strong>Annexure A – Workshop Agenda</strong></h3>
<h4>May 26, 2016</h4>
<table>
<tbody>
<tr>
<td>
<p>9:00-9:30</p>
</td>
<td>
<p><strong>Registration</strong></p>
</td>
</tr>
<tr>
<td>
<p>9:30-10:00</p>
</td>
<td>
<p>Prof. Dinesh Abrol - <em>Welcome</em><br />
<em>Self-introduction and expectations of participants</em><br />
Dr. Usha Ramanathan - <em>Overview of the Workshop</em></p>
</td>
</tr>
<tr>
<td>
<p>10:00-11:00</p>
</td>
<td>
<p><strong>Session 1: Current Status of Aadhaar</strong><br />
Dr. Usha Ramanathan, Legal Researcher, New Delhi - <em>What the 2016 Law Says, and How it Came into Being</em><br />
S. Prasanna, Advocate, New Delhi - <em>Status and Force of Supreme Court Orders on Aadhaar</em><br /> <em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>11:00-11:30</p>
</td>
<td>
<p><strong>Tea Break</strong></p>
</td>
</tr>
<tr>
<td>
<p>11:30-13:30</p>
</td>
<td>
<p><strong>Session 2: Direct Benefits Transfers</strong><br />
Prof. Reetika Khera, Indian Institute of Technology, Delhi - <em>Welfare Needs Aadhaar like a Fish Needs a Bicycle</em><br />
Prof. R. Ramakumar, Tata Institute of Social Sciences, Mumbai - <em>Aadhaar and the Social Sector: A critical analysis of the claims of benefits and inclusion</em><br />
Ashok Rao, Delhi Science Forum - <em>Cash Transfers Study</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>13:30-14:30</p>
</td>
<td>
<p><strong>Lunch</strong></p>
</td>
</tr>
<tr>
<td>
<p>14:30-16:00</p>
</td>
<td>
<p><strong>Session 3: Aadhaar: Science, Technology, and Security</strong><br />
Prof. Subashis Banerjee, Dept of Computer Science & Engineering, IIT, Delhi - <em>Privacy and Security Issues Related to the Aadhaar Act</em><br />
Pukhraj Singh, Former National Cyber Security Manager, Aadhaar, New Delhi - <em>Aadhaar: Security and Surveillance Dimensions</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>16:00-16:30</p>
</td>
<td>
<p><strong>Tea Break</strong></p>
</td>
</tr>
<tr>
<td>
<p>16:30-17:30</p>
</td>
<td>
<p><strong>Session 4: Aadhaar - International Dimensions</strong><br />
Joshita Pai, Center for Communication Governance, National Law University, Delhi - <em>Biometrics and Mandatory IDs in Other Parts of the World</em><br />
Dr. Gopal Krishna, Citizens Forum for Civil Liberties - <em>International Dimensions of Aadhaar</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>17:30-18:00</p>
</td>
<td>
<p><strong>High Tea</strong></p>
</td>
</tr>
</tbody>
</table>
<h4>May 27, 2016</h4>
<table>
<tbody>
<tr>
<td>
<p>9:30-11:00</p>
</td>
<td>
<p><strong>Session 5: Privacy, Surveillance and Ethical Dimensions of Aadhaar</strong><br />
Prabir Purkayastha, Free Software Movement of India, New Delhi - <em>Surveillance Capitalism and the Commodification of Personal Data</em><br />
Arjun Jayakumar, SFLC - <em>Surveillance Projects Amalgamated</em><br />
Col Mathew Thomas, Bengaluru - <em>The Deceit of Aadhaar<em></em><br />
<em>Discussion</em></em></p>
<em>
</em></td>
</tr>
<tr>
<td>
<p>11:00-11:30</p>
</td>
<td>
<p><strong>Tea Break</strong></p>
</td>
</tr>
<tr>
<td>
<p><em>11:30-13:00</em></p>
</td>
<td>
<p><strong>Session 6: Aadhaar - Broad Issues I</strong><br />
Prof. G Nagarjuna, Homi Bhabha Center for Science Education, Tata Institute of Fundamental Research, Mumbai - <em>How to prevent linked data in the context of Aadhaar</em><br />
Dr. Anupam Saraph, Pune - <em>Aadhaar and Moneylaundering</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>13:00-14:00</p>
</td>
<td>
<p><strong>Lunch</strong></p>
</td>
</tr>
<tr>
<td>
<p>14:00-15:30</p>
</td>
<td>
<p><strong>Session 7: Aadhaar - Broad Issues II</strong><br />
Prof. MS Sriram, Visiting Faculty, Indian Institute of Management, Bangalore - <em>Financial lnclusion</em><br />
Nikhil Dey, MKSS, Rajasthan - <em>Field witness: Technology on the Ground</em><br />
Prof. Himanshu, Centre for Economic Studies & Planning, JNU - <em>UID Process and Financial Inclusion</em><br />
<em>Discussion</em></p>
</td>
</tr>
<tr>
<td>
<p>15:30-16:00</p>
</td>
<td>
<p><strong>Session 8: Conclusion</strong></p>
</td>
</tr>
<tr>
<td>
<p>16:00-18:00</p>
</td>
<td>
<p><strong>Informal Meetings</strong></p>
</td>
</tr>
</tbody>
</table>
<h3 id="AB" style="text-align: justify;"><strong>Annexure B – Workshop Participants</strong></h3>
<p>Anjali Bhardwaj, Satark Nagrik Sangathan</p>
<p>Dr. Anupam Saraph</p>
<p>Arjun Jayakumar, Software Freedom Law Centre</p>
<p>Ashok Rao, Delhi Science Forum</p>
<p>Prof. Chinmayi Arun, National Law University, Delhi</p>
<p>Prof. Dinesh Abrol, Jawaharlal Nehru University</p>
<p>Prof. G Nagarjuna, Homi Bhabha Center for Science Education, Tata Institute of Fundamental Research, Mumbai</p>
<p>Dr. Gopal Krishna, Citizens Forum for Civil Liberties</p>
<p>Prof. Himanshu, Jawaharlal Nehru University</p>
<p>Japreet Grewal, the Centre for Internet and Society</p>
<p>Joshita Pai, National Law University, Delhi</p>
<p>Malini Chakravarty, Centre for Budget and Governance Accountability</p>
<p>Col. Mathew Thomas</p>
<p>Prof. MS Sriram, Indian Institute of Management, Bangalore</p>
<p>Nikhil Dey, Mazdoor Kisan Shakti Sangathan</p>
<p>Prabir Purkayastha, Knowledge Commons and Free Software Movement of India</p>
<p>Pukhraj Singh, Bhujang</p>
<p>Rajiv Mishra, Jawaharlal Nehru University</p>
<p>Prof. R Ramakumar, Tata Institute of Social Sciences, Mumbai</p>
<p>Dr. Reetika Khera, Indian Institute of Technology, Delhi</p>
<p>Dr. Ritajyoti Bandyopadhyay, Indian Institute of Science Education and Research, Mohali</p>
<p>S. Prasanna, Advocate</p>
<p>Sanjay Kumar, Science Journalist</p>
<p>Sharath, Software Freedom Law Centre</p>
<p>Shivangi Narayan, Jawaharlal Nehru University</p>
<p>Prof. Subhashis Banerjee, Indian Institute of Technology, Delhi</p>
<p>Sumandro Chattapadhyay, the Centre for Internet and Society</p>
<p>Dr. Usha Ramanathan, Legal Researcher</p>
<p><em>Note: This list is only indicative, and not exhaustive.</em></p>
<hr />
<p><a name="ftn1"><strong>[1]</strong></a> Civil Appeal No. 4853 of 2014</p>
<p><a name="ftn2"><strong>[2]</strong></a> WP(C) 494/2012</p>
<p><a name="ftn3"><strong>[3]</strong> </a>. WP(C) 829/2013</p>
<p><a name="ftn4"><strong>[4]</strong></a> WP(C) 833/2013</p>
<p><a name="ftn5"><strong>[5]</strong></a> WP (C) 37/2015; (Earlier intervened in the Aruna Roy petition in 2013)</p>
<p><a name="ftn6"><strong>[6]</strong></a> WP (C) 932/2015</p>
<p><a name="ftn7"><strong>[7]</strong></a> Transferred from Madras HC 2013.</p>
<p style="text-align: justify;"><a name="ftn8"><strong>[8]</strong></a> SLP (Crl) 2524/2014 filed against the order of the Goa Bench of the Bombay HC in CRLWP 10/2014 wherein the High Court had directed UIDAI to share biometric information held by them of all residents of a particular place in Goa to help with a criminal investigation in a case involving charges of rape and sexual assault.</p>
<p><a name="ftn9"><strong>[9]</strong></a> See :http://scroll.in/article/806243/rajasthan-presses-on-with-aadhaar-after-fingerprint-readers-fail-well-buy-iris-scanners</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges'>http://editors.cis-india.org/internet-governance/blog/report-on-understanding-aadhaar-and-its-new-challenges</a>
</p>
No publisherJapreet Grewal, Vanya Rakesh, Sumandro Chattapadhyay, and Elonnai HickockBig DataData SystemsPrivacyResearchers at WorkInternet GovernanceAadhaarWelfare GovernanceBiometricsBig Data for DevelopmentUID2019-03-16T04:42:52ZBlog EntryBig Data Governance Frameworks for 'Data Revolution for Sustainable Development'
http://editors.cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development
<b>A key component of the process to achieve the Sustainable Development Goals is the call for a global 'data revolution' to better understand, monitor, and implement development interventions. Recently there has been several international proposals to use big data, along with reconfigured national statistical systems, to operationalise this 'data revolution for sustainable development.' This analysis by Meera Manoj highlights the different models of collection, management, sharing, and governance of global development data that are being discussed.</b>
<p> </p>
<p><strong>1.</strong> <a href="#1">What are the Sustainable Development Goals?</a></p>
<p><strong>2.</strong> <a href="#2">The Need for a Data Revolution</a></p>
<p><strong>3.</strong> <a href="#3">Big Data: Characteristics and Use for Development</a></p>
<p><strong>3.1.</strong> <a href="#3-1">Characteristics of Big Data</a></p>
<p><strong>3.2.</strong> <a href="#3-2">Using Big Data for Development</a></p>
<p><strong>4.</strong> <a href="#4">Sustainable Development and Data Rights</a></p>
<p><strong>5.</strong> <a href="#5">Governance Frameworks Proposed</a></p>
<p><strong>5.1.</strong> <a href="#5-1">UN Sustainable Development Solutions Network</a></p>
<p><strong>5.2.</strong> <a href="#5-2">The UN DATA Revolution Group</a></p>
<p><strong>5.3.</strong> <a href="#5-3">Organization for Economic Co-Operation and Development</a></p>
<p><strong>5.4.</strong> <a href="#5-4">The Global Partnership for Sustainable Development of Data</a></p>
<p><strong>5.5.</strong> <a href="#5-5">The World Economic Forum (WEF)</a></p>
<p><strong>5.6.</strong> <a href="#5-6">Dr. Julia Lane - A Quadruple Data Helix</a></p>
<p><strong>5.7.</strong> <a href="#5-7">Data Pop Alliance</a></p>
<p><strong>6.</strong> <a href="#6">Conclusion</a></p>
<p><strong>7.</strong> <a href="#7">Endnotes</a></p>
<p><strong>8.</strong> <a href="#8">Author Profile</a></p>
<hr />
<p>Speaking on Big Data, Dan Ariely commented that, "<em>Everyone talks about it, nobody really knows how to do it, and everyone thinks everyone else is doing it, so everyone claims they are doing it</em>" <strong>[1]</strong>. This offers a useful insight into the lack of adequate discourse on the kind of governance and accountability frameworks that are needed to facilitate the developmental, sustainable, and responsible uses of big data.</p>
<p>In light of the recent international proposals to use big data to track the Sustainable Development Goals, this paper highlights the different models of management, sharing, and governance of data that are being discussed, and concurrently, how they conceptualise the various rights around big data and how are they to be protected.</p>
<p> </p>
<h2 id="1">1. What are the Sustainable Development Goals?</h2>
<p>The Sustainable Development Goals, otherwise known as the Global Goals, build on the Millennium Development Goals (MDGs). Adopted on 1 January 2016, these universally applicable 17 goals of the 2030 Agenda for Sustainable Development, seek to end all forms of poverty, fight inequalities, tackle climate change and address a range of social needs like education, health, social protection and job opportunities over the next 15 years <strong>[2]</strong>.</p>
<p> </p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_un-sdg.png" alt="Sustainable Development Goals" />
<h6>Source: UN Data Revolution Group, <em><a href="http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf">A World that Counts</a></em>, 2014, p.12.<br /></h6>
<p> </p>
<h2 id="2">2. The Need for a Data Revolution</h2>
<p>An overwhelming cause of concern regarding the precursor to the SDGs, the MDGs, is the data unavailability to monitor their progress. For instance, the figure below indicates that there is no five-year period when the availability of MDG related data is more than 70% of what is required. Entire groups of people and key issues remain invisible <strong>[3]</strong>. Lack of data is not only a problem for global statisticians, but also for people whose needs and demands remain invisible due to lack of quantitative representation of the same. For instance, the incidences of gender related crimes when not recorded could lead to a misconception on the achievement of the MDG of gender equality.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_undrg_mdg-data.png" alt="UN Stats - Percentage of MDG data currently available for developing countries by nature of source." />
<h6>Source: UN, <a href="http://i0.wp.com/www.un.org/sustainabledevelopment/wp-content/uploads/2015/12/english_SDG_17goals_poster_all_languages_with_UN_emblem_1.png">Sustainable Development Goals</a>.<br /></h6>
<p>As the new goals (SDGs) cover a wider range of issues it is clear that a far higher level of detail is required. To this effect the High-Level Panel of Eminent Persons on the post-2015 agenda has called for a "data revolution for sustainable development" <strong>[4]</strong>.</p>
<p>The world is experiencing a Data Revolution and a "data deluge." One estimate has it that 90% of the data in the world has been created in the last 2 years. As Eric Schmidt of Google in 2010 famously said, "<em>There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days</em> <strong>[5]</strong>.</p>
<p>In its report <em>A World that Counts</em>, the UN Data Revolution Group defines the data revolution as an explosion in the volume of data, the speed with which data are produced, the number of producers of data, the dissemination of data, and the range of things on which there is data, coming from new technologies such as mobile phones and the “internet of things”, and from other sources, such as qualitative data, citizen-generated data and perceptions data <strong>[6]</strong>.</p>
<p>This data revolution in the context of sustainable development has been defined by the UN Secretary General’s Independent Expert Advisory Group (IEAG) as follows:</p>
<blockquote>[T]he integration of data coming from new technologies with traditional data in order to produce relevant high‐quality information with more details and at higher frequencies to foster and monitor sustainable development. This revolution also entails the increase in accessibility to data through much more openness and transparency, and ultimately more empowered people for better policies, better decisions and greater participation and accountability, leading to better outcomes for the people and the planet <strong>[7]</strong>.</blockquote>
<p>The majority of such “data coming from new technologies” is what can be called big data. It is data being generated in real-time, in high velocity and volume, in a variety of forms and formats, and on an increasing range of phenomenon that are being mediated by digital technologies – from governance to human communication. Further, a good part of such big data is not about the content of the phenomenon concerned but about its process – for example, Call Detail Records are generated for each mobile phone call a person makes and it contains data about the process of the call (time, location, duration, recipient, etc.) but not about the content of the call. Big data about various governmental and human processes are becoming a crucial instrument for documenting and monitoring of the same.</p>
<p> </p>
<h2 id="3">3. Big Data: Characteristics and Use for Development</h2>
<h3 id="3-1">3.1. Characteristics of Big Data</h3>
<p>The simplest definition of big data is that it is a dataset of more than 1 petabyte. The US Bureau of Labour Statistics terms it to be non-sampled data, characterized by the creation of databases from electronic sources whose primary purpose is something other than statistical inference <strong>[8]</strong>.</p>
<p>The characteristics which broadly distinguish Big Data are sometimes called the “3 V’s”: more volume, more variety and higher rates of velocity <strong>[9]</strong>. Big data sources generally share some or all of these features <strong>[10]</strong>:</p>
<ul><li>Digitally generated,</li>
<li>Passively produced,</li>
<li>Automatically collected,</li>
<li>Geographically or temporally trackable, and</li>
<li>Continuously analysed.</li></ul>
<p>Increasingly, Big Data is recognised as creating "new possibilities for international development" <strong>[11]</strong>. It could provide faster, cheaper, more granular data and help meet growing and changing demands. It was claimed, for example, that "<em>Google knows or is in a position to know more about France than INSEE</em>" <strong>[12]</strong>, its highly resourceful national statistical agency. To illustrate, Global Pulse gives the example of a hypothetical small household facing soaring commodity prices, particularly food and fuel <strong>[13]</strong>. They have the options of:</p>
<ul><li>Getting part of their food at a nearby World Food Programme distribution centre,</li>
<li>Reducing mobile usage,</li>
<li>Temporarily taking their children out of school,</li>
<li>Calling a health hotline when children show signs of malnutrition related diseases, and</li>
<li>Venting about their frustration on social media.</li></ul>
<p>Such a systemic shock of food insecurity will prompt thousands of households to react in roughly similar ways. These collective behavioural changes may show up in different digital data sources:</p>
<ul><li>WFP might record that it serves twice as many meals a day,</li>
<li>The local mobile operator may see reduced usage,</li>
<li>UNICEF data may indicate that school attendance has dropped,</li>
<li>Health hotlines might see increased volumes of calls reporting malnutrition, and</li>
<li>Tweets mentioning the difficulty to “afford food” might begin to rise.</li></ul>
<p>Thus the power of real-time, digital data to predict paths for development is immense. Amassing such a large volume of data which tracks practically every aspect of social behavious can revolutionize the field of official statistics and policy making.</p>
<p>Two points to be noted are: 1) all these data sources are not available for comparison in the real-time by default, so one task before using big data in developmental work is to make data from different sources available across agencies and make them comparable, and 2) finding repeating patterns within large data sets, sourced from varied origins, can not only allow for monitoring but also (statistically) predicting future possibilities and implications for development action.</p>
<h3 id="3-2">3.2. Using Big Data for Development</h3>
<p>There are several international organizations attempting to use such data.</p>
<p>Global Pulse, a United Nations initiative, launched by the Secretary-General in 2009, seeks to leverage innovations in digital data, rapid data collection and analysis to help decision-makers gain a real-time understanding of how crises impact vulnerable populations. To this end, Global Pulse is establishing an integrated, global network of Pulse Labs, anchored in Pulse Lab New York, to pilot the approach at country level <strong>[14]</strong>.</p>
<p>The Global Working Group on Big Data for Official Statistics, created in May 2014, pursuant to Statistical Commission, makes an inventory of ongoing activities and examples regarding the use of big data, addresses concerns related to methodology, human resources, quality and confidentiality, and develops guidelines on classifying various types of big data sources <strong>[15]</strong>.</p>
<p>There have been applications even on a national and individual level. For instance, in 2013, various sources reported that the CIA had admitted to the “full monitoring of Facebook, Twitter, and other social networks” to identify links between events and sequences or paths leading to national security threats, ultimately leading to forecasting future activities and events <strong>[16]</strong>.</p>
<p>In the field of conflict prevention is the emerging applications to map and analyse unstructured data generated by politically active Internet use by academics, activists, civil society organizations, and even general citizens. In reference to Iran’s post-election crisis beginning in 2009, it is possible to detect web-based usage of terms that reflect a general shift from awareness towards mobilization, and eventually action within the population <strong>[17]</strong>.</p>
<p>The "Big Data, Small Credit" report proposes that financial inclusion can be promoted by allowing consumers with mobile phones to access credit formally as customers <strong>[18]</strong>.</p>
<p>At a national level, the biggest challenge for most big data projects is the limited or restricted access the government agencies have to potential big data sets owned by the private sector <strong>[19]</strong>. The overall consensus is that Big Data to track SDGs must complement traditional data sources <strong>[20]</strong>. This is because big data may not always be available for the entire population, or include a diverse enough sample of the population. Moreover most big data projects measure development indicators through a correlation which may not always be correct unlike official data. For instance big data might help in predicting lowered household income through reducing mobile bills while traditional data directly collects income statistics.</p>
<p>In a survey by the Global Working Group on Big Data for Official Statistics <strong>[21]</strong>, it was found that only a few countries have developed a long-term vision for the use of big data, while many are formulating a big data strategy. Most countries have not yet defined business processes for integrating big data sources and results into their work and do not have a defined structure for managing big data projects.</p>
<p>Thus there exists a need to identify a governance framework for big data for sustainable development, not only at national level, but also at the international level.</p>
<p> </p>
<h2 id="4">4. Sustainable Development and Data Rights</h2>
<p>Any discussion on governance frameworks would be incomplete without defining the kind of data rights they must seek to protect.</p>
<p>In the famous parable of the six blind men and the elephant they conclude that the elephant is like a wall, snake, spear, tree, fan or rope, depending upon where they touch. Similarly Internet experiences of individual users (what they touch) often contrast drastically with different views (what they conclude) on what would constitute data rights.</p>
<p>The IEAG in its report has identified the following set of data related rights, but has not defined any actual framework or process for ensuring them (yet) <strong>[22]</strong>:</p>
<ul><li>Right to be counted,</li>
<li>Right to an identity,</li>
<li>Right to privacy and to ownership of personal data,</li>
<li>Right to due process (for example when data is used as evidence in proceedings, or in administrative decisions),</li>
<li>Freedom of expression,</li>
<li>Right to participation,</li>
<li>Right to non-discrimination and equality, and</li>
<li>Principles of consent.</li></ul>
<p>Personal data is broadly defined as "<em>any information relating to an identified or identifiable individual</em>" <strong>[23]</strong>. Often primary data producers (users of services and devices generating data) are unaware of individual privacy infringements <strong>[24]</strong>.</p>
<p>A survey by the Global Working Group on Big Data for Official Statistics found that only a few countries have a specific privacy framework for big data, while most apply the privacy framework for traditional statistics to big data as well <strong>[25]</strong>.</p>
<p>Conventionally, safeguards against the re-use of big data to protect data rights have involved the “anonymization” or “de-identification” of data, to conceal individual identities. Global Pulse, for instance, is putting forth the concept of Data Philanthropy, whereby "<em>corporations 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 real-time or near real-time</em>" <strong>[26]</strong>. There however exists a debate on whether data can actually be anonymized effectively. Several state that data can never be effectively de-anonymized due to technological challenges <strong>[27]</strong>. For instance, when the New York City government released de-anonymised data sets of New York cab drivers were made re-identifiable by approaching a separate method. Within less than 2 hours work, researchers knew which driver drove every single trip in this entire dataset. It would be even be easy to calculate drivers’ gross income, or infer where they live <strong>[28]</strong>.</p>
<p>Even the OECD opines that the current model of limiting identifiability of individuals is unsustainable. It recommends moving towards one where the focus is on transparency around how data is being used, rather than preventing specific types of use, stating that - "<em>research funding agencies and data protection authorities should collaborate to develop an internationally recognized framework code of conduct covering the use of new forms of personal data, particularly those generated via network communication. This framework, built on best practice procedures for consent from data subjects, data sharing and re-use, anonymization methods, etc., could be adapted as necessary for specific national circumstances</em>" <strong>[29]</strong>.</p>
<p>Thus, there is a push for the arguement that the historical approaches to protecting privacy and confidentiality — namely, <em>informed consent</em> and <em>anonymity</em> — no longer hold <strong>[30]</strong>. Some have even suggested using big data itself to keep track of user permissions for each piece of data to act as a legal contract <strong>[31]</strong>.</p>
<p>There is an overall consensus that any legal or regulatory mechanisms set up to mobilise the 'data revolution for sustainable development' should protect the data rights of the people <strong>[32]</strong>, without any clear agreement on what these rights may be.</p>
<p> </p>
<h2 id="5">5. Governance Frameworks Proposed</h2>
<p>A largely unanswered question that is posed in light of the emerging consensus on the use of Big Data for monitoring SDGs is within what sort of governance frameworks these data collection and analysis methods will operate. Methods of collection and the key actors involved in data analysis, management, storage and coordination. The role of NGOs and CSOs, if any, within these systems must be delineated. Certain key global organizations and eminent researchers have suggested the following models.</p>
<h3 id="5-1">5.1. UN Sustainable Development Solutions Network</h3>
<p>In 2012, the UN Secretary-General launched the UN Sustainable Development Solutions Network (SDSN) to mobilize global scientific and technological expertise to promote practical problem solving for sustainable development, including the design and implementation of the Sustainable Development Goals (SDGs) <strong>[33]</strong>. It has proposed the following.</p>
<p><strong>Collection</strong></p>
<p>The Inter-Agency and Expert Group on Sustainable Development Goal Indicators (IAEGSDG) and the United Nations Statistical Commission are to establish roadmaps for strengthening specific data collection tools that enable the monitoring of SDG indicators.</p>
<p><strong>Analysis</strong></p>
<p>Based on discussions with a large number of statistical offices, including Eurostat, BPS Indonesia, the OECD, the Philippines, the UK, and many others, 100 is recommended to be the maximum number of global indicators to analyse data for which NSOs can report and communicate effectively in a harmonized manner. This conclusion was strongly endorsed during the 46th UN Statistical Commission and the Expert Group Meeting on SDG indicators <strong>[34]</strong>.</p>
<p>Specialist indicators developed by thematic communities must be used for data analysis as they include input and process metrics that are helpful complements to official indicators, which tend to be more outcome-focused. For example, the UN Inter-Agency Group on Child Mortality Estimation has developed a specialist hub responsible for analysing, checking, and improving mortality estimation. This is a leading source for child morality information for both governments and non-governmental actors <strong>[35]</strong>.</p>
<p>Research arms of private companies such as Microsoft Research, IBM research, SAS, and R&D arms of telecom companies could directly partner with official statistical systems to share sophisticated analysing techniques <strong>[36]</strong>.</p>
<p><strong>Management</strong></p>
<p>Four levels of monitoring, national, regional, global, and thematic, should be "<em>organized in an integrated architecture</em>" <strong>[37]</strong>.</p>
<p>Countries must decide individually whether official data must be complemented with non-official indicators from big data which can add richness to the monitoring of the SDGs.</p>
<p>Where possible, regional monitoring should build on existing regional mechanisms, such as the Regional Economic Commissions, the Africa Peer Review Mechanism, or the Asia-Pacific Forum on Sustainable Development <strong>[38]</strong>.</p>
<p>To coordinate thematic monitoring under the SDGs, each thematic initiative may have one or more lead specialist agencies or “custodians” as per the IAEG-MDG monitoring processes. Lead agencies would be responsible for convening multi-stakeholder groups, compiling detailed thematic reports, and encouraging ongoing dialogues on innovation. These thematic groups can become testing grounds in launching a data revolution for the SDGs, trialling new measurements and metrics that in time can feed into the global monitoring process with annual reports <strong>[39]</strong>.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_unsdsn_monitoring.png" alt="UN Sustainable Development Solutions Network - Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring." />
<h6>Schematic illustration with explanation of the indicators for national, regional, global, and thematic monitoring.<br />Source: UN Sustainable Development Solutions Network, <em><a href="http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf">Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a Data Revolution for the SDGs</a></em>, 2015, p.3.<br /></h6>
<p><strong>Role of NSOs</strong></p>
<p>Monitoring the SDG agenda will require substantive improvements in national statistical capacity. Assessments of existing capacity to fulfil SDG monitoring expectations must be undertaken and needs be integrated into National Strategies for the Development of Statistics (NSDSs) <strong>[40]</strong>.</p>
<p><strong>Coordination</strong></p>
<p>A Global Partnership for Sustainable Development Data must be established and a World Forum on Sustainable Development Data be convened in 2016 to create mechanisms for ongoing collaboration and innovation.</p>
<p>A high-level, powerful group of businesses and states must convene the various data and transparency sustainable development initiatives under one umbrella.</p>
<p>To ensure comparability, Global Monitoring Indicators must be harmonized across countries by one lead technical or specialist agency which will additionally coordinate data standards and collection and provide technical support.</p>
<p>The following table indicates the suggested Lead Agencies for individual SDGs <strong>[41]</strong>.</p>
<table>
<tbody>
<tr>
<td><strong>Number</strong></td>
<td><strong>Sustainable Development Goal</strong></td>
<td><strong>Lead Agencies</strong></td>
</tr>
<tr>
<td>1.</td>
<td>No Poverty</td>
<td>World Bank, UNDP, UNSD, UNICEF, ILO, FAO, UN-Habitat, UNISDR, WHO, CRED, UNFPA, and UN Population Division</td>
</tr>
<tr>
<td>2.</td>
<td>No Hunger</td>
<td>FAO, WHO, UNICEF, and Internal Fertilizer Industry Associaton (IFA)</td>
</tr>
<tr>
<td>3.</td>
<td>Good Health</td>
<td>WHO, UN Population Division, UNICEF, World Bank, GAVI, UN AIDS, and UN-Habitat</td>
</tr>
<tr>
<td>4.</td>
<td>Quality Education</td>
<td>UNESCO, UNICEF, and World Bank</td>
</tr>
<tr>
<td>5.</td>
<td>Gender Equality</td>
<td>UNICEF, UN Women, WHO, UNSD, ILO, UN Population Division, and UNFPA</td>
</tr>
<tr>
<td>6.</td>
<td>Clean Water and Sanitation</td>
<td>WHO/UNICEF Joint Monitoring Programme (JMP), FAO, UN Water, and UNEP</td>
</tr>
<tr>
<td>7.</td>
<td>Renewable Energy</td>
<td>Sustainable Energy for All, IEA, WHO, World Bank, and UNFCC</td>
</tr>
<tr>
<td>8.</td>
<td>Good Jobs and Economic Growth</td>
<td>IMF, World Bank, UNSD, and ILO</td>
</tr>
<tr>
<td>9.</td>
<td>Innovation and Infrastructure</td>
<td>World Bank, OECD, UNIDO, UNFCC, UNESCO, and ITU</td>
</tr>
<tr>
<td>10.</td>
<td>Reduced Inequalities</td>
<td>UNSD, World Bank, and OECD</td>
</tr>
<tr>
<td>11.</td>
<td>Sustainable Cities and Communities</td>
<td>UN-Habitat, Global City Indicators Facility, WHO, CRED, UNISDR, FAO, and UNEP</td>
</tr>
<tr>
<td>12.</td>
<td>Responsible Consumption</td>
<td>EITI, UNCTAD, UN Global Compact, FAO, UNEP Ozone Secretariat, WBCSD, GRI, IIRC, and Global Compact</td>
</tr>
<tr>
<td>13.</td>
<td>Climate Action</td>
<td>OECD DAC, UNFCCC, and IEA</td>
</tr>
<tr>
<td>14.</td>
<td>Life below Water</td>
<td>UNEP-WCMC, IUCN, and FMC</td>
</tr>
<tr>
<td>15.</td>
<td>Life on Land</td>
<td>FAO, UNEP, IUCN, and UNEP- WCMC</td>
</tr>
<tr>
<td>16.</td>
<td>Peace and Justice</td>
<td>UNODC, WHO, UNOCHA, UNCHR, IOM, OCHA, OECD, UN Global Compact, EITI, UNCTAD, UNICEF, UNESCO, and Transparency International</td>
</tr>
<tr>
<td>17.</td>
<td>Partnership for the Goals</td>
<td>BIS, IASB, IFRS, IMF, WIPO, WTO, UNSD, OECD, World Bank, OECD DAC, and SDSN</td>
</tr>
</tbody>
</table>
<h3 id="5-2">5.2. The UN DATA Revolution Group</h3>
<p>The group constituted by the UN Secretary-General Ban Ki-moon in August 2014, is an Independent Expert Advisory Group with the aim of making concrete recommendations on bringing about a 'data revolution for sustainable development' <strong>[42]</strong>. In its report, <em>A World that Counts</em>, it makes the following recommendations <strong>[43]</strong>.</p>
<p><strong>Collection</strong></p>
<p>Clear standards on data collection methods must be developed based on the UN Fundamental Principles of Official Statistics. Periodic audits must be conducted by professional and independent third parties to ensure data quality.</p>
<p>Governments, civil society, academia and the philanthropic sector must work together strengthening statistical literacy so that all people have capacity to input into and evaluate the quality of data.</p>
<p>Social entrepreneurs, private sector, academia, media, civil society and other individuals and institutions must be engaged globally with incentives (prizes, data challenges) to encourage data sharing.</p>
<p><strong>Analysis</strong></p>
<p>A SDGs Analysis and Visualisation Platform is to be set up for fostering private-public partnerships and community-led peer-production efforts for data analysis.</p>
<p>A dashboard on ”the state of the world” will engage the UN, think-tanks, academics and NGOs in analysing, and auditing data.</p>
<p>Academics and scientists are to analyse data to provide long-term perspectives, knowledge and data resources at all levels.</p>
<p>The “Global Forum of SDG-Data Users” will ensure feedback loops between data producers, processors and users to improve the usefulness of data and information produced.</p>
<p>A “SDGs data lab” to support the development of a first wave of SDG indicators is to be established mobilizing key public, private and civil society data providers, academics and stakeholders working with the Sustainable Development Solutions Network.</p>
<p><strong>Storage</strong></p>
<p>A “world statistics cloud” will store data and metadata produced by different institutions but according to common standards, rules and specifications.</p>
<p><strong>Role of NSOs</strong></p>
<p>Civil society organisations must share data and processing methods with private and public counterparts on the basis of agreements. They must hold governments and companies accountable using evidence on the impact of their actions, provide feedback to data producers, develop data literacy and help communities and individuals generate and use data.</p>
<p>NSOs are the central players of the Data Revolution. Their autonomy must be strengthened to maintain data quality. They must abandon expensive and cumbersome production processes, incorporate new data sources like big data that is human and machine-readable, compatible with geospatial information systems and available quickly enough to ensure that the data cycle matches the decision cycle. Collaborations with the private sector can boost technical and financial investments.</p>
<p><strong>Coordination</strong></p>
<p>Key stakeholders must create a “Global Consensus on Data”, to adopt principles concerning legal, technical, privacy, geospatial and statistical standards. Best practices related to public data such as the Open Government Partnership (OGP) and the G8 Open Data Charter are recommended foundations for such principles.</p>
<p>A UN-led “Global Partnership for Sustainable Development Data” is proposed, to coordinate and broker key global public-private partnerships for data sharing <strong>[44]</strong>.</p>
<p>A “World Forum on Sustainable Development Data” and “Network of Data Innovation Networks” will be a converging point for the data ecosystem to share ideas and experiences for improvements, innovation and technology transfer.</p>
<h3 id="5-3">5.3. Organization for Economic Co-Operation and Development (OECD)</h3>
<p>The Organisation for Economic Co-operation and Development (OECD) is an inter-governmental organization that seeks to promote policies that will improve the economic and social well-being of people globally. It has made the following proposals <strong>[45]</strong>.</p>
<p><strong>Collection</strong></p>
<p>Data is to be collected from National statistical agencies, national and international researchers and international organisations.</p>
<p><strong>Role of NSOs</strong></p>
<p>By leveraging the expertise of telecommunications companies and software developers, for instance, national statistical systems could potentially reduce costs and improve the availability of data to monitor development goals <strong>[46]</strong>.</p>
<p><strong>Coordination</strong></p>
<p>National Data Forums for Social Science Data must be created for the development of social science data for improved coordination between social scientists, data producers (national statistical agencies, government departments, large private sector businesses and sources undertaking academic direction), and data curators.</p>
<p>Social science research communities must contribute to national plans of action after a needs assessment <strong>[47]</strong>. Research funding agencies must collaborate at the international level for a common system for referencing datasets in research publications <strong>[48]</strong>.</p>
<h3 id="5-4">5.4. The Global Partnership for Sustainable Development of Data</h3>
<p>The partnership is a global network of governments, NGOs, and businesses working to strengthen the inclusivity, trust, and innovation in the way that data is used to address the world’s sustainable development efforts <strong>[49]</strong>.</p>
<p><strong>Analysis</strong></p>
<p>There must be a common framework for information processing. At minimum, a simple lexicon must tag each datum specifying:</p>
<ul><li><strong>What:</strong> i.e. the type of information contained in the data,</li>
<li><strong>Who:</strong> the observer or reporter,</li>
<li><strong>How:</strong> the channel through which the data was acquired,</li>
<li><strong>How much:</strong> whether the data is quantitative or qualitative, and</li>
<li><strong>Where and when:</strong> the spatio-temporal granularity of the data.</li></ul>
<p>Analysis of data involves filtering relevant information, summarising keywords and categorising into indicators. This intensive mining of socioeconomic data, known as “reality mining,” can be done by: (1) Continuous analysis of real time streaming data, (2) Digestion of semi-structured and unstructured data to determine perceptions, needs and wants. (3) Real-time correlation of streaming data with slowly accessible historical data repositories.</p>
<p>Use of big data for developmental goals can draw upon all three techniques to various degrees depending on availability of data and the specific needs.</p>
<p><strong>Role of NSOs</strong></p>
<p>NSOs have a pivotal part to play in the data revolution. Countries and organizations believe that big data cannot replace traditional official statistical data as it is based more on perception than facts. To quote Winston Churchill, "<em>Do not trust any statistics that you did not fake yourself</em>."</p>
<p>For instance, a study found that Google Flu Trends, to detect influenza epidemics, predicted nonspecific flu-like respiratory illnesses well but not actual flu. The mismatch was due to popular misconceptions on influenza symptoms. This has important policy implications. Doctors using Google Flu Trends may overstock on flu vaccines or be overly inclined to diagnose normal respiratory illnesses as influenza <strong>[50]</strong>.</p>
<p>However Big Data if understood correctly, can inform where further targeted investigation is necessary and give immediate responses to favourably change outcomes.</p>
<h3 id="5-5">5.5. The World Economic Forum (WEF)</h3>
<p>The WEF is an International Organization for Public-Private Cooperation. It engages the foremost political, business and other leaders of society to shape global, regional and industry agendas <strong>[51]</strong>. In the report titled <em>Big Data, Big Impact: New Possibilities for International Development</em>, it makes the following recommendations <strong>[52]</strong>.</p>
<p><strong>Collection</strong></p>
<p>Data production and development actors include individuals, public sector and the private sector. Each produce different kinds of data that have unique requirements. The private sector maintains vast troves of transactional data, much of which is "data exhaust," or data created as a by-product of other transactions. The public sector maintains enormous datasets in the form of census data, health indicators, and tax and expenditure information. The following figure highlights the different kinds of data that each sector collects and what incentives they have to share the data along with requirements to maintain such data.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_wef_01.png" alt="" />
<h6>World Economic Forum - Diagram on Data Commons.<br />
Source: World Economic Forum, <em><a href="http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf">Big Data, Big Impact: New Possibilities for International Development</a></em>, 2012, p.4.<br /></h6>
<p>Business models must be created to provide the appropriate incentives for private-sector actors to share data. Such models already exist in the Internet environment. For instance companies in search and social networking profit from products they offer at no charge to end users because the usage data these products generate is valuable to other ecosystem actors. Similar models could be created in garnering Big Data for SDGs. The following flowchart illustrates how different sectors must work together to incentivise data collection and sharing.</p>
<img src="https://raw.githubusercontent.com/cis-india/website/master/img/big-data-gov-framework_wef_02.png" alt="" />
<h6>World Economic Forum - Diagram on Global Coordination.<br />
Source: World Economic Forum, <em><a href="http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf">Big Data, Big Impact: New Possibilities for International Development</a></em>, 2012, p.7.<br /></h6>
<h3 id="5-6">5.6. Dr. Julia Lane - A Quadruple Data Helix</h3>
<p>Dr. Julia Lane is a Professor in the Wagner School of Public Policy at New York University; and also a Provostial Fellow in Innovation Analytics and a Professor in the Center for Urban Science and Policy <strong>[53]</strong>. She has done extensive research on the uses of big data. In her paper titled "Big Data for Public Policy: A Quadruple Data Helix," she makes the following suggestions <strong>[54]</strong>.</p>
<p><strong>Collection</strong></p>
<p>In the future there will exist a model of a quadruple data helix for data collection which will have four strands — state and city agencies, universities, private data providers, and federal agencies.i</p>
<p>A new set of institution, city/university data facilities, must be established. These institutions should form the backbone of the quadruple helix, with direct connections to the private sector and to the federal statistical agencies.</p>
<p><strong>Analysis</strong></p>
<p>There is a need for graduate training for non-traditional students, who need to understand how to use data science tools as part of their regular employment. They must identify and capture the appropriate data, understand how data science models and tools can be applied, and determine how associated errors and limitations can be identified from a social science perspective.i</p>
<p>Universities can act as a trusted independent third party to process, store, analyze, and disseminate data. ii</p>
<p><strong>Management</strong></p>
<p>The new infrastructure must ensure that data from disparate sources are collected managed and used in a manner that is informed by end users. There are many technical challenges: disparate data sets must be ingested, their provenance determined, and metadata documented. Researchers must be able to query data sets to know what data are available and how they can be used. And if data sets are to be joined, they must be joined in a scientific manner, which means that workflows need to be traced and managed in such a way that the research can be replicated.</p>
<p><strong>Coordination</strong></p>
<p>The role of State and City agencies is to address immediate policy issues, rather than to build long-term data infrastructures as their mandate is to work with city data than the full spectrum of available data.</p>
<h3 id="5-7">5.7. Data-Pop Alliance</h3>
<p>Data-Pop Alliance is a global coalition on Big Data and development created by the Harvard Humanitarian Initiative, MIT Media Lab, and Overseas Development Institute that brings together researchers, experts, practitioners, and activists to promote a people-centred big data revolution through collaborative research, capacity building, and community engagement <strong>[55]</strong>. It makes the following suggestions.</p>
<p><strong>Collection</strong></p>
<p>The idea of <em>shared responsibility</em> between the public and private sector is a proposed operational principles to create a deliberative space. Mechanisms and legal frameworks must be devised for private companies to share their big data under formalized and stable arrangements instead of being compelled by ad hoc requests from researchers and policymakers.</p>
<p>The media too, could avoid publishing statistical data collected by unexplained methodologies by employing "statistical editors" and disseminate verified information.</p>
<p><strong>Role of NSOs</strong></p>
<p>For official statistics, engaging with Big Data is not a technical consideration but a political obligation. In a two tier system of official and non-official statistics, the public and investors tend to distrust official figures. For instance, the results of the 2010 census in the UK are being disputed on the basis of sewage data.</p>
<p>It is imperative for NSOs to retain, or regain, their primary role as the legitimate custodian of knowledge and creator of a deliberative public space to democratically drive human development <strong>[56]</strong>.</p>
<p> </p>
<h2 id="6">6. Conclusion</h2>
<p>The Big data frameworks provide some useful insights on monitoring mechanisms though some questions remain unanswered in each model. Key actors that have been proposed include city and state agencies like NSOs, private companies, social scientists, private individuals and international research agencies. Data analysis can be through public-private collaborations, data philanthropy, and using indicators by thematic communities.</p>
<p><strong>Collection</strong></p>
<p>There appears consensus across models that collection must be effected through public private partnerships while providing incentives.</p>
<p><strong>Analysis</strong></p>
<p>While several methods of analysis have been proposed by the Global Partnership it is unclear on who will be conducting the analysis. The UNSDSN has suggested that it be conducted by academics and scientists with Julia Lane stating it must be through public private partnerships which appear more feasible and transparent.</p>
<p><strong>Role of NSOs</strong></p>
<p>All frameworks agree on the pivotal role of NSOs and acknowledge them as the key players and coordinators at the national level. They must be strengthened financially, technologically and politically. Most frameworks seek to empower national agencies which will coordinate collaborations with the private sector through incentives while protecting personal data.</p>
<p><strong>Coordination</strong></p>
<p>Several international fora have been proposed to enable coordination while there is consensus that the NSOs. A Global Partnership for Sustainable Development Data, a Global Consensus on Data and a World Forum on Sustainable Development Data have been suggested. UN organizations appear to be suggesting more responsibility for those in the UN framework with UNSDSN giving an extensive list of lead agencies (UNDP, UN Women, Who etc) while the WEF emphasises on the private sector, Data Pop Alliance on NSOs, and Prof. Lane on State and City agencies.</p>
<p>On an international level countries can opt to join international organization that are being setup for the purpose. It remains to be seen whether all countries globally can achieve such a feat in a coordinated manner without infringing on data rights when unanswerable to any set international organization. The burden appears to fall on civil society and market forces within the private sector to regulate this process. For instance when a private sector company starts providing large un-anonymized data sets for government use, the privacy concerns of civil society that result in them opting for the company’s competitor’s more privacy friendly products will result in a regulation through market forces. However these forces may have disparate strengths in different contexts and countries depending on market practices and information asymmetry resulting in the lack of a uniform accountability mechanism.</p>
<p> </p>
<h2 id="7">7. Endnotes</h2>
<p><strong>[1]</strong> Dan Ariely, Facebook, January 06, 2013, <a href="https://www.facebook.com/dan.ariely/posts/904383595868">https://www.facebook.com/dan.ariely/posts/904383595868</a>.</p>
<p><strong>[2]</strong> United Nations Organizations, 'Sustainable Development Goals' (United Nations Sustainable Development, 26 September 2015), <a href="http://www.un.org/sustainabledevelopment/sustainable-development-goals/">http://www.un.org/sustainabledevelopment/sustainable-development-goals/</a>, accessed 6 June 2016.</p>
<p><strong>[3]</strong> Data Revolution Group, 'A World that Counts: Mobilising the Data Revolution for Sustainable Development' (November 2014), <a href="http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf">http://www.undatarevolution.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[4]</strong> High level panel on the post-2015 development agenda , 'A New Global Partnership: Eradicate Poverty and Transform Economies through Sustainable Development'(Post2015hlp,0rg, July 2012), <a href="http://www.post2015hlp.org/">http://www.post2015hlp.org/</a>, accessed 8 June 2016.</p>
<p><strong>[5]</strong> Gary King, 'Ensuring the Data-Rich Future of the Social Sciences' [2011] 3(2) Science, <a href="http://gking.harvard.edu/files/datarich.pdf">http://gking.harvard.edu/files/datarich.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[6]</strong> See <strong>[3]</strong>.</p>
<p><strong>[7]</strong> Ibid.</p>
<p><strong>[8]</strong> Michael Horrigan, 'Big Data: A Perspective from the BLS' (Amstatorg, 1 January 2013) <a href="http://magazine.amstat.org/blog/2013/01/01/sci-policy-jan2013/">http://magazine.amstat.org/blog/2013/01/01/sci-policy-jan2013/</a>, accessed 4 June 2016.</p>
<p><strong>[9]</strong> UN Global Pulse, 'Big Data for Development: Challenges & Opportunities' (6 May 2012) <a href="http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf">http://www.unglobalpulse.org/sites/default/files/BigDataforDevelopment-UNGlobalPulseJune2012.pdf</a>, accessed 5 June 2016.</p>
<p><strong>[10]</strong> Emmanuel Letouzé and Johannes Jütting, 'Official Statistics, Big Data and Human Development: Towards a New Conceptual and Operational Approach' (2014) 12(3), Data-Pop Alliance White papers Series, <a href="https://www.odi.org/sites/odi.org.uk/files/odi-assets/events-documents/5161.pdf">https://www.odi.org/sites/odi.org.uk/files/odi-assets/events-documents/5161.pdf</a>, accessed 4 June 2016.</p>
<p><strong>[11]</strong> See <strong>[9]</strong>.</p>
<p><strong>[12]</strong> See <strong>[10]</strong>.</p>
<p><strong>[13]</strong> See <strong>[9]</strong>.</p>
<p><strong>[14]</strong> UN Global Pulse, 'About: United Nations Global Pulse' (2016) <a href="http://www.unglobalpulse.org/about-new">http://www.unglobalpulse.org/about-new</a>, accessed 7 June 2016.</p>
<p><strong>[15]</strong> UN Stats, 'Global Working Group' (2014) <a href="http://unstats.un.org/unsd/bigdata/">http://unstats.un.org/unsd/bigdata/</a>, accessed 8 June 2016.</p>
<p><strong>[16]</strong> New York City Press Release, ‘Mayor Bloomberg, Police Commissioner Kelly and Microsoft Unveil New, State-of-the-Art Law Enforcement Technology that Aggregates and Analyzes Existing Public Safety Data in Real Time to Provide a Comprehensive View of Potential Threats and Criminal Activity’ (New York City, 8 August 2012), <a href="http://www1.nyc.gov/office-of-the-mayor/news/291-12/mayor-bloomberg-police-commissioner-kelly-microsoft-new-state-of-the-art-law">http://www1.nyc.gov/office-of-the-mayor/news/291-12/mayor-bloomberg-police-commissioner-kelly-microsoft-new-state-of-the-art-law</a>, accessed 2 July 2016.</p>
<p><strong>[17]</strong> Francesco Mancini, 'New Technology and the Prevention of Violence and Conflict' (Reliefwebint, April 2013), <a href="http://reliefweb.int/sites/reliefweb.int/files/resources/ipi-e-pub-nw-technology-conflict-prevention-advance.pdf">http://reliefweb.int/sites/reliefweb.int/files/resources/ipi-e-pub-nw-technology-conflict-prevention-advance.pdf</a>, accessed 2 July 2016.</p>
<p><strong>[18]</strong> Arjuna Costa, Anamitra Deb, and Michael Kubzansky, 'Big Data, Small Credit: The Digital Revolution and Its Impact on Emerging Market Consumers,' (Omidyar, 3 March 2013) <a href="https://www.omidyar.com/sites/default/files/file_archive/insights/Big%20Data,%20Small%20Credit%20Report%202015/BDSC_Digital%20Final_RV.pdf">https://www.omidyar.com/sites/default/files/file_archive/insights/Big%20Data,%20Small%20Credit%20Report%202015/BDSC_Digital%20Final_RV.pdf</a>, accessed 2 July 2016.</p>
<p><strong>[19]</strong> United Nations Economic and Social Council, 'Report of the Global Working Group on Big Data for Official Statistics' (UN Stats, 3 March 2015), <a href="http://unstats.un.org/unsd/statcom/doc15/2015-4-BigData-E.pdf">http://unstats.un.org/unsd/statcom/doc15/2015-4-BigData-E.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[20]</strong> Ibid.</p>
<p><strong>[21]</strong> Ibid.</p>
<p><strong>[22]</strong> See <strong>[3]</strong>.</p>
<p><strong>[23]</strong> OECD, 'OECD Guidelines on the Protection of Privacy and Transborder Flows of Personal Data' (23 September 1980), <a href="http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm">http://www.oecd.org/sti/ieconomy/oecdguidelinesontheprotectionofprivacyandtransborderflowsofpersonaldata.htm</a>, accessed 29 May 2016.</p>
<p><strong>[24]</strong> Amir Efrati, ''Like' Button Follows Web Users' (WSJ, 18 May 2011) <a href="http://www.wsj.com/articles/SB10001424052748704281504576329441432995616">http://www.wsj.com/articles/SB10001424052748704281504576329441432995616</a>, accessed 23 May 2016.</p>
<p><strong>[25]</strong> See <strong>[15]</strong>.</p>
<p><strong>[26]</strong> Robert Kirkpatrick, 'Data Philanthropy: Public and Private Sector Data Sharing for Global Resilience' (UN Global Pulse, 16 September 2011), <a href="http://www.unglobalpulse.org/blog/data-philanthropy-public-private-sector-data-sharing-global-resilience">http://www.unglobalpulse.org/blog/data-philanthropy-public-private-sector-data-sharing-global-resilience</a>, accessed 4 June 2016.</p>
<p><strong>[27]</strong> Ibid.</p>
<p><strong>[28]</strong> Arvind Narayanan, 'No silver bullet: De-identification still doesn't work' (1 April 2016), <a href="http://randomwalker.info/publications/no-silver-bullet-de-identification.pdf">http://randomwalker.info/publications/no-silver-bullet-de-identification.pdf</a>, accessed 3 July 2016.</p>
<p><strong>[29]</strong> OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives,' (February 2013) <a href="http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf">http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf</a>, accessed 2 June 2016.</p>
<p><strong>[30]</strong> S. Barocas, 'The Limits of Anonymity and Consent in the Big Data Age,' in <em>Privacy, Big Data, and the public good: Frameworks for Engagement</em> (Cambridge University Press, 2014).</p>
<p><strong>[31]</strong> A. Pentland, 'Institutional Controls: The New Deal on Data,' in <em>Privacy, Big Data, and the public good: Frameworks for Engagement</em> (Cambridge University Press, 2014).</p>
<p><strong>[32]</strong> See <strong>[3]</strong>.</p>
<p><strong>[33]</strong> UN Sustainable Development Solutions Network, 'About Us: Vision and Organization' (2012) <a href="http://unsdsn.org/about-us/vision-and-organization/">http://unsdsn.org/about-us/vision-and-organization/</a>, accessed 2 June 2016.</p>
<p><strong>[34]</strong> UN Sustainable Development Solutions Network, 'Indicators and a Monitoring Framework for the Sustainable Development Goals: Launching a data revolution for the SDGs' (12 June 2015) <a href="http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf">http://unsdsn.org/wp-content/uploads/2015/05/150612-FINAL-SDSN-Indicator-Report1.pdf</a>, accessed 4 June 2016.</p>
<p><strong>[35]</strong> UNICEF, 'CME Info - Child Mortality Estimates' (2014) <a href="http://www.childmortality.org/">http://www.childmortality.org/</a>, accessed 1 June 2016.</p>
<p><strong>[36]</strong> See <strong>[10]</strong>.</p>
<p><strong>[37]</strong> UNESCO, 'Technical report by the Bureau of the United Nations Statistical Commission (UNSC) on the process of the development of an indicator framework for the goals and targets of the post-2015 development agenda' (6 March 2015) <a href="http://www.uis.unesco.org/ScienceTechnology/Documents/unsc-post-2015-draft-indicators.pdf">http://www.uis.unesco.org/ScienceTechnology/Documents/unsc-post-2015-draft-indicators.pdf</a>, accessed 3 June 2016.</p>
<p><strong>[38]</strong> UN, 'The Road to Dignity by 2030: Ending Poverty, Transforming All Lives and Protecting the Planet ' (4 December 2014) <a href="http://www.un.org/disabilities/documents/reports/SG_Synthesis_Report_Road_to_Dignity_by_2030.pdf">http://www.un.org/disabilities/documents/reports/SG_Synthesis_Report_Road_to_Dignity_by_2030.pdf</a>, accessed 7 June 2016.</p>
<p><strong>[39]</strong> Ibid.</p>
<p><strong>[40]</strong> UN Sustainable Development Solutions Network, 'Data for Development: An Action Plan to Finance the Data Revolution for Sustainable Development' (10 July 2015) <a href="http://unsdsn.org/wp-content/uploads/2015/04/Data-For-Development-An-Action-Plan-July-2015.pdf">http://unsdsn.org/wp-content/uploads/2015/04/Data-For-Development-An-Action-Plan-July-2015.pdf</a>, accessed 3 June 2016.</p>
<p><strong>[41]</strong> See <strong>[34]</strong>.</p>
<p><strong>[42]</strong> UN Data Revolution Group, 'About the Independent Expert Advisory Group' (6 November 2014) <a href="http://www.undatarevolution.org/about-ieag/">http://www.undatarevolution.org/about-ieag/</a>, accessed 4 June 2016.</p>
<p><strong>[43]</strong> See <strong>[3]</strong>.</p>
<p><strong>[44]</strong> The Partnership has already been established, and it is developing a further framework.</p>
<p><strong>[45]</strong> Organisation for Economic Co-Operation and Development), 'The Organisation for Economic Co-operation and Development (OECD): About' (2016) <a href="http://www.oecd.org/about/">http://www.oecd.org/about/</a>, accessed 2 June 2016.</p>
<p><strong>[46]</strong> Organisation for Economic Co-Operation and Development, 'Strengthening National Statistical Systems to Monitor Global Goals' (2015) <a href="http://www.oecd.org/dac/POST-2015%20P21.pdf">http://www.oecd.org/dac/POST-2015%20P21.pdf</a>, accessed 1 June 2016.</p>
<p><strong>[47]</strong> Ibid.</p>
<p><strong>[48]</strong> OECD Global Science Forum, 'New Data for Understanding the Human Condition: International Perspectives' (February 2013) <a href="http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf">http://www.oecd.org/sti/sci-tech/new-data-for-understanding-the-human-condition.pdf</a>, accessed 2 June 2016.</p>
<p><strong>[49]</strong> The Global Partnership On Sustainable Development Data, 'Who We Are: The Data Ecosystem and the Global Partnership' (2016) <a href="http://www.data4sdgs.org/who-we-are/">http://www.data4sdgs.org/who-we-are/</a>, accessed 5 June 2016.</p>
<p><strong>[50]</strong> World Economic Forum, 'Big Data, Big Impact: New Possibilities for International Development' (22 January 2012) <a href="http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf">http://www3.weforum.org/docs/WEF_TC_MFS_BigDataBigImpact_Briefing_2012.pdf</a>, accessed 8 June 2016.</p>
<p><strong>[51]</strong> World Economic Forum, 'Our Mission: The World Economic Forum' (12 January 2016) <a href="https://www.weforum.org/about/world-economic-forum/">https://www.weforum.org/about/world-economic-forum/</a>, accessed 7 June 2016.</p>
<p><strong>[52]</strong> See <strong>[50]</strong>.</p>
<p><strong>[53]</strong> Julia Lane, Homepage, <a href="http://www.julialane.org/">http://www.julialane.org/</a>.</p>
<p><strong>[54]</strong> Julia Lane, 'Big Data for Public Policy: The Quadruple Helix' (2016) 8(1) <em>Journal of Policy Analysis and Management</em>, <a href="http://onlinelibrary.wiley.com/doi/10.1002/pam.21921/abstract">DOI:10.1002/pam.21921</a>, accessed 1 June 2016.</p>
<p><strong>[55]</strong> Data-Pop Alliance, 'Data-Pop Alliance: Our Mission' (May 2014) <a href="http://datapopalliance.org/">http://datapopalliance.org/</a>, accessed 1 June 2016.</p>
<p><strong>[56]</strong> See <strong>[10]</strong>.</p>
<p> </p>
<h2 id="8">8. Author Profile</h2>
<p>Meera Manoj is a law student at the Gujarat National Law University, Gandhinagar and has completed her first year. She is passionate about civil rights, feminism, economics in law and anything involving paneer. She aspires to travel the world and build up a vast library, with unparalleled sections on International Law and Archie comics.</p>
<p> </p>
<p>
For more details visit <a href='http://editors.cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development'>http://editors.cis-india.org/internet-governance/blog/big-data-governance-frameworks-for-data-revolution-for-sustainable-development</a>
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No publisherMeera ManojDevelopmentBig DataData SystemsInternet GovernanceBig Data for DevelopmentSustainable Development Goals2016-07-05T13:13:32ZBlog Entry