Account Aggregator System and Privacy
Context:
As economies digitise rapidly, embedding regulatory principles directly into digital infrastructure has emerged as an effective way to address data privacy challenges.
More on News:
- India’s Account Aggregator (AA) framework is a prime example of this techno-legal approach, successfully integrating privacy principles into its core design.
- However, while the system has achieved significant milestones, it faces limitations that require innovative solutions to ensure comprehensive data protection.
Account aggregators (AAs) are digital platforms that facilitate the secure sharing and consolidation of financial data from various sources, such as banks, insurance companies, and investment firms. They play a crucial role in enhancing financial management by allowing users to access a comprehensive overview of their financial information in one place.
India’s Account Aggregator Framework:
- The AA Framework is a consent-based data-sharing system designed to facilitate secure access to financial data.
- It has already processed nearly 125 million data-sharing consents, enabling diverse financial products.
- At its heart is a technological construct called the “consent artefact,” which ensures that privacy principles—such as informed consent, purpose specification, data minimisation, and retention restrictions—are upheld in every transaction.
- When a financial information user (FIU), such as a lender, requests access to data, they must use this artefact to obtain consent from the data principal (the individual whose data is being accessed).
- The data is then shared by the financial information provider (FIP) only for the agreed-upon purposes and within the stipulated boundaries.
- This system aligns with global data protection standards, ensuring that personal data processed through the AA framework respects privacy principles by design.
The Missing Link:
- Once data is transferred from the FIP to the FIU, there is no technical measure to prevent the latter from using it for unauthorised purposes.
- The consent artefact secures a commitment from FIUs to adhere to the agreed terms, but enforcement of this commitment relies on trust rather than programmatic safeguards.
Introducing Financial Information Compute Units (FICUs):
A recent proposal outlines a solution to address this shortcoming by leveraging advanced cryptographic techniques and secure processing environments.
This mechanism introduces Financial Information Compute Units (FICUs)—specialised nodes capable of securely processing encrypted data.
Here’s how the system would work:
- Encrypted Data Sharding: Data requested under a consent artefact would be encrypted and split into shards, which are distributed across multiple FICU nodes.
- No single node has access to enough data to derive meaningful insights.
- Purpose-Bound Computation: FIUs seeking insights must submit computation requests in the form of secure, purpose-bound operational codes.
- These codes specify the exact computations to be performed on the data.
- Consent Validation: Before any computation, the operational codes are inspected by a consent prover, an entity responsible for verifying their alignment with the consent artefact.
- This ensures that computations adhere strictly to the user’s approved purposes.
- Secure Multi-Party Computation: Using distributed multi-party computation (MPC) techniques, FICU nodes process the encrypted data shards to generate insights.
- Crucially, the raw data remains encrypted and inaccessible throughout the process.
Building a Comprehensive Data Privacy System:
Integrating FICUs into the AA framework would create a holistic data protection system, addressing current gaps in use-limitation enforcement. However, implementing this solution requires new institutional structures and governance mechanisms:
- Consent Approvers: A new category of entities would need to verify and approve operational codes submitted by FIUs.
- Multi-Stakeholder Governance: Effective governance systems must ensure that FICU nodes are operated by diverse entities with varied incentives. This diversity minimises the risk of collusion or unauthorised data processing.
