Secure Multi-Party Computation with TEE Encrypted Data Linking
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Solution Overview
Problem
The challenge of cross-platform linking of user data while preserving privacy is hindered by heterogenous cloud architectures and regulatory restrictions, with existing browser-based trusted helpers being insufficiently robust across various use cases.
Innovation Solution
Implementing a trusted execution environment that performs query processing on double-encrypted data sets from multiple software application back ends using homomorphic encryption, with a double-encrypted identifier as the join key, ensuring privacy preservation through double encryption with different private keys.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If browser-based trusted helpers are used for cross-platform data linking, then data aggregation capability is improved, but security and privacy preservation are insufficient
Solution Approach 1:
The patent introduces a trusted execution environment (TEE) as an intermediary between multiple cloud platforms. The TEE performs secure multi-party computation by receiving encrypted data from different platforms, executing join operations on encrypted identifiers, and returning results without exposing plaintext data. This mediator approach enables cross-platform data linking while maintaining security and privacy, resolving the contradiction between aggregation capability and security preservation.
2Reliability
If double encryption with different private keys is applied to preserve privacy, then privacy preservation is improved, but computational complexity increases
Solution Approach 1:
The patent extracts the computationally intensive decryption and join operations from the client side and relocates them to a server-side trusted execution environment. Clients only need to perform simple encryption of identifiers using their own private keys, while the TEE handles the complex operations of receiving encrypted data from multiple parties, performing joins on encrypted identifiers, and computing results. This extraction approach maintains strong privacy preservation through double encryption while reducing client computational complexity.
3Productivity
If cross-platform data linking is implemented to enable data aggregation, then data utility is improved, but heterogenous cloud architectures and regulatory restrictions create barriers
Solution Approach 1:
The patent implements a universal protocol for secure multi-party computation that can interface with multiple different cloud platform architectures. The TEE receives encrypted data in a standardized format from various heterogenous platforms, performs join operations using encrypted identifiers as join keys, and returns results that can be used across different platforms. This universal approach enables cross-platform data linking and aggregation while accommodating heterogenous cloud architectures and regulatory restrictions through encrypted computation.
Data Source
AI summary
Technologies for secure multi-party computation include computing first double-encrypted data, computing second double-encrypted data, and, in a trusted execution environment, executing a query on the first double-encrypted data and the second double encrypted data to create a query-processed double-encrypted data set. The trusted execution environment can provide the query-processed double-encrypted data set to a requester such as another computer, system, or process.