Homomorphic Evaluation of Tensor Programs for FHE Optimization
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Solution Overview
Problem
The integration of Fully Homomorphic Encryption (FHE) in cloud computing is inefficient due to high costs and operational noise, with conventional techniques requiring manual determination of optimal encryption schemes, leading to human error, time consumption, and inefficient resource utilization.
Innovation Solution
The optimization of FHE-adapted tensor circuits and corresponding specifications through deep-neural network inference computations, which automate the generation and selection of cost-efficient encryption parameters and data layouts for performing homomorphic encryption operations, reducing overhead costs and improving performance and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional fully homomorphic encryption schemes are used, then data security and privacy are improved, but operational noise and computing costs increase
Solution Approach 1:
The patent applies parameter changes by automatically selecting and optimizing encryption parameters (such as polynomial degree, modulus size, and noise thresholds) based on the specific computational task requirements. This allows the system to adjust security parameters dynamically to achieve the minimum necessary security level while minimizing operational noise and energy consumption.
2Reliability
If manual determination of optimal encryption schemes is performed, then security is improved, but time consumption and human error increase
Solution Approach 1:
The system implements self-service by automatically determining optimal encryption schemes without human intervention. The automated parameter selection process evaluates multiple encryption configurations and selects the optimal parameters based on predefined security criteria and performance metrics, eliminating manual configuration time and human error while maintaining security standards.
Solution Approach 2:
The patent applies preliminary action by pre-configuring and pre-evaluating multiple encryption parameter sets before actual computation. The system performs preliminary analysis to determine optimal parameters in advance, so that when encryption operations are needed, the best parameters are already selected and ready for immediate use, significantly reducing time consumption.
3Reliability
If multiple encryption parameter sets are evaluated, then optimal security is achieved, but computing resource consumption increases
Solution Approach 1:
The system implements feedback mechanisms that continuously monitor the performance and security characteristics of different encryption parameter sets. Based on this feedback, the system iteratively refines parameter selection, eliminating inefficient configurations early in the evaluation process and focusing computational resources only on promising parameter sets that meet security thresholds.
Solution Approach 2:
The patent applies partial action by evaluating only the necessary subset of encryption parameter sets rather than exhaustively testing all possible configurations. The system uses heuristic criteria and security thresholds to identify and evaluate only the most relevant parameter combinations, achieving optimal security while significantly reducing computing resource consumption compared to complete brute-force evaluation.
Data Source
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AI summary
Embodiments provide for optimizing the generation, evaluation, and selection of tensor circuit specifications for a tensor circuit to perform homomorphic encryption operations on encrypted data. A computing device having an improved compiler and runtime configuration can obtain a tensor circuit and associated schema. The computing device can map the obtained tensor circuit to an equivalent tensor circuit, adapted to perform fully homomorphic encryption (FHE) operations, and instantiated based on the obtained associated scheme. The computing device can then monitor a flow of data through the equivalent FHE-adapted tensor circuit utilizing various tensor circuit specifications determined therefor. A cost of each tensor circuit specification can be determined by the computing device based on the monitored flow of data, so as to identify an optimal set of optimal tensor circuit specifications that can be employed by the obtained tensor circuit, to efficiently perform homomorphic encryption operations on encrypted data.