Homomorphic Encryption Parameter Optimization
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Solution Overview
Problem
Existing homomorphic encryption schemes face challenges in processing speed and storage requirements, particularly in fully homomorphic encryption, which incurs significant costs and is unworkable due to deep circuits and substantial storage needs, leading to slower throughput and latency when evaluating encrypted data.
Innovation Solution
The implementation of optimized encoding techniques and parameter selection using an error growth simulator to encode fractional parts of real numbers, encode numbers in balanced odd bases, and utilize slot encoding, which reduces processing and memory requirements, and eliminates the need for bootstrapping operations, ensuring data confidentiality and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fully homomorphic encryption is used to maintain data confidentiality during remote computations, then security is improved, but processing speed deteriorates due to deep circuits and substantial storage needs
Solution Approach 1:
The patent applies parameter changes by optimizing the encoding scheme parameters (such as the dimension parameter k and modulus parameter q) to achieve a balance between security and processing efficiency. By carefully selecting and adjusting these parameters, the system maintains strong encryption while reducing computational overhead and improving processing speed.
Solution Approach 2:
The patent segments the homomorphic encryption process into distinct phases: encoding phase (performed by client), computation phase (performed by server on encrypted data), and decoding phase (performed by client). This segmentation allows the computationally intensive parts to be distributed appropriately, with the server only needing to perform simple homomorphic operations on encrypted data while the client handles the more complex encoding and decoding.
2Reliability
If fully homomorphic encryption is used to secure remote computations, then data security is improved, but storage requirements increase substantially
Solution Approach 1:
The patent reduces storage requirements by optimizing the encoding parameters, particularly by selecting an appropriate dimension parameter k that balances security strength with ciphertext size. The parameter selection process explicitly considers storage constraints while maintaining security requirements.
3Productivity
If optimized encoding techniques are used to improve processing speed, then productivity is improved, but encoding complexity increases
Solution Approach 1:
The patent uses polynomial-based encoding where data is represented as coefficients of polynomials. This algebraic structure allows for efficient homomorphic operations while the complexity of the encoding is managed through systematic polynomial arithmetic rather than ad-hoc complex transformations.
Data Source
AI summary
The techniques and/or systems described herein are directed to improvements in homomorphic encryption to improve processing speed and storage requirements. For example, the techniques and/or systems can be used on a client device to encode data to be sent to a remote server, to be operated on while maintaining confidentiality of data. The encoding scheme can be optimized by automatically selecting one or more parameters using an error growth simulator based on an actual program that operates on the encoded data. For example, the simulator can be used iteratively to determine an optimized parameter set which allows for improved homomorphic operations while maintaining security and confidentiality of a user's data.