Homomorphic Encryption Encoding With Pattern-Based Operation Pruning
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Solution Overview
Problem
Homomorphic encryption (HE) operations are costly and inefficient due to high computational overhead, particularly in encoding processes, which constitute approximately 35% of the overall processing time, and require significant memory resources, being 3-5 orders of magnitude slower than plaintext operations.
Innovation Solution
The method identifies patterns in input data to prune unnecessary computations, replacing them with previously generated results or predetermined values, optimizing encoding processes by eliminating redundant operations such as zero results, duplicates, or conjugates, and generating optimized encoding code for execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If homomorphic encryption encoding is performed using traditional methods, then data security is maintained, but processing speed is extremely slow and computational overhead is high
Solution Approach 1:
The system performs preliminary analysis of input data patterns before encoding to identify opportunities for optimization. By pre-classifying data patterns and determining which operations can be pruned or replaced with cached results, the system prepares optimization strategies in advance that significantly speed up the subsequent encoding process without compromising security
Solution Approach 2:
The system dynamically changes encoding parameters based on detected input patterns. By classifying data into different pattern categories and selecting optimized encoding paths for each class, the system adapts the encoding process to minimize computational overhead while maintaining the required security properties of homomorphic encryption
2Productivity
If complete encoding operations are performed on all input data, then encoding accuracy is maintained, but processing time increases significantly
Solution Approach 1:
The system extracts and identifies redundant operations in the encoding process by analyzing input data patterns. By detecting patterns that will produce predictable results (such as zero outputs or duplicate values), the system removes unnecessary computation steps while preserving the accuracy of the final encoded output
Solution Approach 2:
The system discards redundant computations by replacing them with cached results from previous operations. By maintaining a cache of previously computed encoding results and identifying when the same operations can be reused, the system recovers computational effort without sacrificing encoding accuracy, significantly reducing overall processing time
3Use of energy by moving object
If redundant operations are eliminated through pattern recognition, then processing speed increases, but system complexity increases due to pattern classification requirements
Solution Approach 1:
The system segments the encoding process into distinct phases: pattern classification, operation identification, and optimized execution. By dividing the complex task into manageable segments with clear boundaries, the system reduces overall complexity while enabling significant energy savings through selective pruning of redundant operations in each segment
Data Source
AI summary
Mechanisms are provided for increasing the speed of an encoding process. The mechanisms classify operations in the encoding process based on learned associations between patterns of input data and corresponding encoding result types generated from encoding the patterns of input data. The mechanisms identify operations in the encoding process that can be pruned based on the classifications, to thereby generate a set of prune operations. In addition, the mechanisms replace operations in the set of prune operations with replacement operations that retrieve a corresponding previously generated result. Moreover, the mechanisms emit optimized encoding code comprising the replacement operations in replacement of the set of prune operations that are pruned, for execution of the encoding process using the optimized encoding code.