Trusted Enclave Analytics for Sensitive Data Privacy
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Solution Overview
Problem
Existing methods for performing analytics on sensitive data often require sharing plaintext data or rely on untrusted third parties, which can compromise privacy and violate regulatory restrictions.
Innovation Solution
Utilizing a trusted execution environment (TEE) within a server to perform privacy-preserving analytics without revealing plaintext data, combined with differential privacy mechanisms to ensure secure data processing and anonymized results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If plaintext data is shared for analytics processing, then analysis accuracy is improved, but data privacy and security are compromised
Solution Approach 1:
The patent introduces a trusted execution environment (TEE) as an intermediary between data owners and analytics processors. The TEE creates a secure enclave that receives encrypted data, processes it using privacy-preserving techniques (such as homomorphic encryption or secure multi-party computation), and returns results without exposing plaintext data to untrusted parties. This mediator enables accurate analytics while maintaining privacy by design.
Solution Approach 2:
The patent transforms data from plaintext to encrypted form before processing, changing the state parameter of the data. By using cryptographic transformations (encryption schemes, differential privacy mechanisms), the data maintains its analytical utility while its sensitive properties are obscured. The data is processed in a different state (encrypted) than the state required for direct analysis, resolving the contradiction between accuracy and privacy.
2Reliability
If a trusted third party is used for data processing, then data security is improved, but system complexity and trust assumptions increase
Solution Approach 1:
The patent enables data owners to perform privacy-preserving analytics themselves using locally executed trusted execution environments or secure computing frameworks. Instead of relying on an external trusted third party, the system allows parties to autonomously process their own encrypted data or collaboratively process aggregated data without exposing it to external trustees. This self-service approach reduces system complexity by eliminating the need for centralized trusted infrastructure.
3Object-affected harmful factors
If encrypted data is processed without TEE, then data privacy is maintained, but processing reliability and verification are compromised
Solution Approach 1:
The patent implements verification mechanisms that provide feedback to data owners about the processing of their encrypted data within the TEE. The TEE generates cryptographic proofs (such as attestation reports or zero-knowledge proofs) that verify the code executed within the enclave and confirm that privacy-preserving operations were performed correctly. This feedback loop maintains privacy while enabling independent verification of processing integrity without requiring trust in the hosting infrastructure.
Data Source
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AI summary
Aspects of the current subject matter are directed to performing privacy-preserving analytics over sensitive data without sharing plaintext data and without requiring a trusted third party. Implementations provide for utilizing a trusted execution environment within a server to compute the privacy-preserving result. Data owners via user devices send their encrypted data directly to an enclave managed by a trusted execution environment, without the server and the cloud service provider for the server seeing the plaintext data. The enclave computes the analytics directly on the data and releases the privacy-preserving result that can be ensured by code analysis and remote attestation from all parties.