Zero-Trust Fusion Computation via Trusted Execution Environment
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Solution Overview
Problem
Current data fusion computation methods across different industries and domains face security risks during transmission, storage, and usage due to reliance on insecure plaintext transmission, varying database quality, and unprotected data in memory, leading to potential data leakage and compliance issues, especially in sensitive privacy data applications.
Innovation Solution
A zero-trust fusion computation method utilizing a Trusted Execution Environment (TEE) for secure data processing, where data is encrypted and processed within an encrypted memory, ensuring that no party needs to be trusted, with self-signed certificates and chip-level encryption for secure transmission and storage, and program integrity verification to prevent tampering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If plaintext transmission mode is used for data transmission, then transmission speed is improved, but data security deteriorates
Solution Approach 1:
The patent segments data into multiple shares using secret sharing technology, where each share alone cannot reveal the original information. This allows data to be transmitted and processed in distributed form, maintaining both speed and security by ensuring that no single point holds the complete sensitive data.
Solution Approach 2:
The patent introduces a trusted execution environment (TEE) as an intermediary that handles encrypted data processing. The TEE acts as a secure mediator that can process secret-shared data without exposing the underlying sensitive information, enabling secure computation while maintaining transmission efficiency.
2Reliability
If TLS encrypted transmission is used to guarantee security, then data security is improved, but transmission efficiency deteriorates
Solution Approach 1:
The patent performs encryption and secret sharing operations in advance during data preparation, so that during transmission and processing, the data is already in a secure format. This preliminary securing of data eliminates the need for continuous encryption/decryption cycles during transmission, maintaining both security and efficiency.
3Ease of operation
If data is stored in general databases for easy access, then ease of operation is improved, but data security deteriorates
Solution Approach 1:
The patent segments sensitive data into multiple shares and stores them in distributed form across different locations or databases. This segmentation allows the system to maintain data accessibility through standard database operations while ensuring that no single database holds the complete sensitive information, thus improving security without sacrificing ease of operation.
4Productivity
If data is loaded into memory for analytical calculation, then computation speed is improved, but data security deteriorates
Solution Approach 1:
The patent introduces a trusted execution environment (TEE) as a secure intermediary that loads and processes secret-shared data in memory. The TEE provides hardware-based isolation and protection, allowing fast in-memory computation while preventing unauthorized access or leakage of the sensitive data during the computation process.
5Reliability
If homomorphic encryption is used for secure multi-party computation, then data security is improved, but operating efficiency deteriorates
Solution Approach 1:
The patent uses secret sharing to segment data into multiple shares that can be processed independently and efficiently. Unlike homomorphic encryption which requires complex cryptographic operations on encrypted data, secret sharing allows for more efficient distributed computation where operations can be performed on the shares themselves, improving operating efficiency while maintaining security.
6Reliability
If federated learning is used for multi-party data training, then data security is improved, but application scenario versatility deteriorates
Solution Approach 1:
The patent creates a universal secure computation platform based on secret sharing and TEE that can handle various types of data processing tasks beyond just model training. This includes data analysis, querying, and other computational operations, making the solution applicable to multiple scenarios including but not limited to federated learning, thereby improving versatility while maintaining security.
Data Source
AI summary
A method and device for zero-trust fusion computation of multi-party data is provided, which adopts a chip-level based trusted execution environment (TEE) technique, and by improving a development preparation phase of a fusion computation background and improving a calculation phase, enable fusion computation of multi-party data to be performed in a zero-trust secure running environment, guaranteeing that the data is in a secure state without trusting any party during an entire process of transmission, storage and fusion computation, and allowing for enhanced data privacy protection. The solution brings many advantages in terms of data storage security, data transmission security and data use security as well as universality and performance superiority.