Encrypted Query Logging for Researcher Privacy
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Solution Overview
Problem
Existing data systems fail to protect the privacy of authorized querying entities when logging queries, as malicious actors can infer identities and intentions from logged data, and current security solutions do not adequately address this issue.
Innovation Solution
A data system that receives and logs encrypted queries using a Camenisch-Lysyanskaya signature digital signature method and cryptographic authentication methods like zk-SNARK or zk-STARK, which allows for verification of authorized entities without revealing their identity, and modifies queries to remove identifying information for secure logging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If queries are logged in a distributed ledger with full visibility to all participants, then query tracking and security monitoring are improved, but researcher identity privacy is compromised
Solution Approach 1:
The system segments the query logging functionality into two distinct components: a public ledger that stores encrypted query data for transparency and tracking, and a private key management system that holds decryption keys solely for the rule enforcer. This segmentation allows query tracking to be maintained while preventing other participants from accessing researcher identities, as the ledger data is encrypted and only the rule enforcer can decrypt it.
Solution Approach 2:
The system introduces an intermediary cryptographic encryption layer between the query logging mechanism and the participants. Queries are encrypted before being written to the ledger, and only the rule enforcer possesses the decryption capability. This intermediary mechanism enables the system to maintain both query visibility for security monitoring and researcher identity privacy, as no other participant can access the encrypted data.
2Object-generated harmful factors
If the ledger is accessible to all participants for transparency, then system accountability is improved, but inference attacks become possible
Solution Approach 1:
The system applies preliminary anti-action by encrypting queries before they are written to the ledger. This preemptive encryption prevents malicious participants from performing inference attacks on the logged data, as the queries are already protected against unauthorized access. The encryption is established before the potential harm can occur, maintaining both accountability through logging and security against inference attacks.
3Loss of information
If permissioned blockchain is used to limit ledger access, then researcher privacy is improved, but trust requirements increase
Solution Approach 1:
The system uses cryptographic encryption as an intermediary mechanism that eliminates the need for trust-based access control. Instead of relying on permissioned blockchain where participants must trust each other not to misuse access, the system encrypts queries and gives decryption keys only to the rule enforcer. This cryptographic intermediary removes the trust requirement while maintaining researcher privacy, as the encryption mechanism objectively prevents unauthorized access without needing social trust agreements.
Data Source
AI summary
A method (100) for logging queries made to a data system (200), comprising: receiving (120), at the data system, an encrypted query from an authorized query entity, wherein the encrypted query is digitally signed by the query entity using a digital signature method, and further wherein the digital signature is encrypted using a cryptographic authentication method; and logging (130), at a query logging database of the data system, at least a portion of the encrypted query.