Trusted Execution Environment for Privacy-Preserving Data Processing
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Solution Overview
Problem
Users are reluctant to share sensitive data due to lack of trust in organizations hosting and processing their information, as existing technologies lack effective solutions for ensuring data privacy and security in computing processes.
Innovation Solution
A method and apparatus utilizing an isolated processor with a trusted execution environment and storage, which receives encrypted data, decrypts it using a private key, performs calculations with an approved program authorized by a smart contract, and provides results, ensuring that both the program and processor are verified by participants of the smart contract, and the public and private keys are generated by the processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users share sensitive data with organizations for processing, then valuable insights and services can be generated, but users lose trust and privacy protection
Solution Approach 1:
The system segments the computing process into two distinct parts: data remains encrypted and stored by the user, while only computation operations are transmitted to and executed by the remote server. This segmentation ensures that sensitive data never leaves the user's control in plaintext form, maintaining trust while enabling productive processing.
Solution Approach 2:
Homomorphic encryption acts as an intermediary mechanism that allows computations to be performed on encrypted data without decryption. The encryption scheme mediates between the user's privacy requirements and the server's computational capabilities, enabling trusted processing without requiring users to trust the server with their raw data.
2Reliability
If homomorphic encryption is used to enable computations on encrypted data, then privacy protection is improved, but the technology is limited to specific research problems and not yet broadly applicable
Solution Approach 1:
The system employs a universal encrypted computing framework that can handle multiple types of computations including machine learning inference, statistical analysis, and data aggregation. By implementing a general-purpose homomorphic encryption scheme (such as BFV or CKKS), the system achieves broad applicability across different problem domains rather than being restricted to specific research scenarios.
3Reliability
If data is encrypted before processing, then privacy is maintained, but computations become more complex and resource-intensive
Solution Approach 1:
The system applies partial decryption or selective decryption techniques where only specific portions of the encrypted data are decrypted when absolutely necessary, or uses decryption-only schemes where the server performs computations and the user simply decrypts the final result. This partial action approach reduces the overall computational complexity compared to performing all operations in full homomorphic encryption mode.
Data Source
AI summary
Apparatus, method, computer program product and computer readable medium are disclosed for trusted computing. A method includes, at an isolated processor including a trusted execution environment and an isolated storage, receiving data encrypted with a first public key; decrypting the encrypted data with a first private key; performing calculation on the decrypted data by using an approved program; and providing the calculation result, wherein the approved program is authorized by a smart contract, a unique identifier of the smart contract is stored in the isolated processor, both the approved program and the isolated processor are verified by at least one participant of the smart contract, and the first public key and the first private key are generated by the isolated processor.