Blockchain Authenticator for Privacy-Preserving Data Verification
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Solution Overview
Problem
Collaborative machine learning model training across non-trusting organizations faces challenges in preserving data privacy while ensuring data authenticity, as anonymization methods fail to verify the accuracy of anonymized data.
Innovation Solution
Implementing an anonymization service within a trusted execution environment (TEE) that generates an authenticator object binding hashes of raw and anonymized data, and submits it to a blockchain ledger via a blockchain transaction, ensuring data privacy and authenticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anonymization methods are used to preserve data privacy, then data privacy is improved, but data authenticity verification capability deteriorates
Solution Approach 1:
The system segments the data handling process into distinct components: raw data storage, anonymization processing, and authenticity verification. The authenticator object separates the hash of raw data from the anonymized data itself, allowing independent verification without exposing sensitive information. This segmentation enables both privacy preservation and authenticity verification to coexist.
Solution Approach 2:
The patent introduces an intermediary mechanism - the authenticator object containing hashes - that mediates between the need for data privacy and the need for authenticity verification. The hash acts as an intermediary representation that verifies data integrity without revealing the actual sensitive data, thus resolving the contradiction between privacy and verification capability.
2Reliability
If blockchain ledger is used to store authenticator objects, then data authenticity is improved, but system complexity increases
Solution Approach 1:
The system extracts only the essential authenticity verification information (hashes of raw data and anonymized data) into the authenticator object stored on the blockchain, rather than storing the entire dataset. This extraction approach provides robust authenticity verification while minimizing the complexity burden on the blockchain system by storing only critical verification data.
Solution Approach 2:
Instead of storing the actual sensitive data on the blockchain, the system stores cryptographic copies (hashes) of the data. These hash copies provide sufficient information for authenticity verification without the complexity and security concerns of storing complete datasets on the distributed ledger.
3Reliability
If trusted execution environment is used for anonymization, then data privacy is improved, but processing overhead increases
Solution Approach 1:
The system performs preliminary anonymization processing within the trusted execution environment before data leaves the secure boundary. By completing the anonymization and generating authenticators in advance within the TEE, the system ensures privacy protection is built-in from the start, avoiding the need for repeated security checks and reprocessing later, thus managing overhead efficiently.
Data Source
AI summary
An example operation may include one or more of anonymizing, via an anonymization service hosted within a trusted execution environment (TEE), raw data provided by a computing node to generate anonymized data, generating, via the anonymization service, an authenticator object that binds together a hash of the raw data and a hash of the anonymized data, transmitting the generated anonymized data to the computing node, and submitting the authenticator object to a blockchain ledger via a blockchain transaction.