Interesting work in India to enforce Data Protection. Useful reference for when we jumpstart that push for our own legislation, and it’s enforcement.
Data Protection: We can innovate, leapfrog
Unlike the European Union, which has more than 37 years of history when it comes to data protection law, India is starting with a near blank slate after the Supreme Court confirmed that privacy is a constitutionally-guaranteed fundamental right in the Puttaswamy case judgement. While we would want to maintain adequacy and compatibility with the EU General Data Protection Regulation (GDPR) because it has become the global standard, we must realise that there is an opportunity for leapfrogging. This article attempts to introduce the reader to three different visions for intermediaries that have emerged within the Indian data protection debate around the accountability principle. I will also provide a brief sketch of an idea that we are developing at the Centre for Internet and Society. This is an incomplete list as there must be more proposals for regulatory innovation around the accountability principle that I am currently unaware of.
n Account Aggregators: The India Stack ecosystem that has been built around the Aadhaar programme first proposed intermediaries called Account Aggregators. Account Aggregators manage consent artifacts. India Stack has traditionally been described as having four layers — presenceless, paperless, cashless and consent. The consent layer is supposed to feature Account Aggregators. If, for example, a data subject wanting an insurance policy visits an insurance portal, the portal would collect personal information and a consent artifact from her and pass it on to multiple insurance companies. These insurance companies would send personalised bids to the portal, which would be displayed on a comparative grid to enable empowered selection.
The data structure consent artifact has been provided in the Master Direction from RBI titled \”Non-Banking Financial Company Account Aggregator Directions,\” published in September 2016. How does this work? The fields includes (i) identity and optional contact information; (ii) nature of the financial information requested; (iii) purpose; (iv) the identity of the recipients, if any; (v) URL/address for notifications when the consent artifact is used; (vi) consent artifact creation date, expiry date, identity and signature/digital signature of the Account Aggregator; and (vii) any other attribute as may be prescribed by the RBI. While Account Aggregators make it frictionless for the grant of consent and also for the harvesting of consent by data controllers, it does not make it easy for you to manage and revoke your consent.
n Data Trusts: Most recently, Na.Vijayashankar, a Bengaluru-based cybersecurity and cyberlaw expert, has proposed intermediaries called Data Trusts registered with the regulator and who (i) will work as escrow agents for the personal data (which would be classified by type for different degrees of protection); (ii) will make privacy notices accessible by translating them into accessible language and formats; (iii) disclose data minimally to different data controllers based on the purpose limitation; (iv) issue tokens or pseudonymous identifiers and monetise the data for the benefit of the data subject. To ensure that Data Trusts truly protect the interests of the data subject, Vijayashankar proposes three requirements: (a) public performance reviews (b) audits by the regulator and (c) \”an arms-length relationship with the data collectors.\” In his proposal, Data Trusts are firms with \”the ability to process a real-time request from the data subject to supply appropriate data to the data collector.\”
n Learned Intermediaries: The Takshashila Institution published a paper titled Beyond Consent: A New Paradigm for Data Protection, authored by Rahul Matthan, partner at the law firm Trilegal. Learned Intermediaries would perform mandatory audits on all data controllers above a particular threshold. Like Vijayashankar, Matthan also requires these intermediaries to be certified by an appropriate authority. The main harm that he focuses on is, bias or discrimination. He proposes three stages of audit which are designed for the age of Big Data and Artificial Intelligence: \”(i) Database Query Review; (ii) Black Box Audits; and (iii) Algorithm Review\”. Matthan also tentatively considers a rating system. Learned Intermediaries are a means to address information asymmetry in the market by making data subjects more aware. The impact of churn on their bottom-lines, it is hoped, will force data controllers to behave in an accountable manner, protecting rights and mitigating harms.
n Consent Brokers: Finally, I have proposed the model of a Consent Broker by modifying the concept of the Account Aggregator. Like the Account Aggregator proposal, we would want a competitive set of consent brokers who will manage consent artifacts for data subjects. However, I believe there should be a 1:1 relationship between data subjects and consent brokers so that the latter compete for the business of data subjects. Like Vijayashankar, I believe that the consent broker must have an \”arms-length distance\” from data controllers and must be prohibited from making any money from them. Consent brokers could also be trusted to take proactive actions for the data subjects, such as access and correction.
The need of the hour is the production of regulatory innovations and robust discussions around them for all the nine privacy principles in the Justice AP Shah committee report — notice, choice and consent, collection limitation, purpose limitation, access and correction, disclosure of information, security, openness and accountability.
(The writer is Executive Director, Centre for Internet and Society, Bengaluru)
Sent on the move.
kictanet mailing list