Homomorphic Encryption Workflow Using TEE for Nonlinear Operations
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Solution Overview
Problem
Existing homomorphic encryption technologies struggle with inefficient and inaccurate nonlinear operations, leading to errors in computing tasks due to improper parameter settings and error propagation during Taylor expansion approximations.
Innovation Solution
A method is introduced that decomposes computing tasks into linear and nonlinear operations, utilizing a trusted execution environment (TEE) to perform nonlinear operations and homomorphic encryption services to handle linear operations, ensuring accurate task execution and reducing error propagation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If Taylor expansion approximation is used for nonlinear operations in homomorphic encryption, then nonlinear computing can be performed, but error propagation occurs leading to computing errors
Solution Approach 1:
The patent segments the computing task into linear operations and nonlinear operations. Linear operations are processed using homomorphic encryption while nonlinear operations are processed using plaintext computing in TEE. This segmentation resolves the contradiction by allowing each type of operation to be handled by the most appropriate method, avoiding error propagation from Taylor expansion while maintaining nonlinear operation capability.
Solution Approach 2:
The patent introduces TEE (Trusted Execution Environment) as an intermediary for processing nonlinear operations. Instead of directly using error-prone Taylor expansion in homomorphic encryption, the system uses TEE to perform plaintext computing for nonlinear operations, then returns results to the homomorphic encryption system. This intermediary approach eliminates error propagation while maintaining versatility.
2Reliability
If all operations are processed using homomorphic encryption, then data security is maintained, but computing efficiency decreases due to approximation errors requiring parameter tuning
Solution Approach 1:
The patent segments operations into two categories: linear operations processed via homomorphic encryption for security, and nonlinear operations processed via plaintext computing in TEE for efficiency. This segmentation resolves the contradiction by optimizing each operation type appropriately rather than applying a uniform approach.
Solution Approach 2:
The patent applies different processing qualities to different parts of the computing task. Homomorphic encryption with its high security but lower efficiency is applied locally to linear operations, while plaintext computing with higher efficiency is applied locally to nonlinear operations in the TEE environment. This local quality differentiation resolves the efficiency-security tradeoff.
3Productivity
If parameter settings are optimized for linear operations, then homomorphic encryption performance improves, but nonlinear operation accuracy deteriorates
Solution Approach 1:
The patent segments the computing workflow so that parameter optimization for homomorphic encryption only affects linear operations. Nonlinear operations are separated and processed in TEE where they don't suffer from the same parameter sensitivity issues. This segmentation allows independent optimization without mutual interference.
Solution Approach 2:
The patent extracts nonlinear operations from the homomorphic encryption processing pipeline and handles them separately in the TEE environment. This extraction removes nonlinear operations from the parameter-sensitive homomorphic encryption path, allowing parameters to be optimized for linear operations without degrading nonlinear operation accuracy.
Data Source
AI summary
Provided are a method and system for improving homomorphic encryption performance based on a trusted execution environment (TEE) which includes: acquiring a computing task; decomposing the computing task into a group of subtasks according to a computing function list in the computing task; decomposing the subtasks in turn according to a list of provided basic homomorphic encryption operators (LoHEO) to obtain a suboperation set; labeling a suboperation as a “linear operation” or a “nonlinear operation” according to whether the suboperation can be directly implemented using an element in the LoHEO; processing each suboperation in turn: for a suboperation labeled as the linear operation, determining a homomorphic computing scheme according to a suboperation task, acquiring and using encrypted computing data for homomorphic encryption computing; for a suboperation labeled as the nonlinear operation, completing plaintext computing in a TEE; and returning an encrypted computing result to a user side.