Homomorphic Codebook Compression for Encrypted Deep Learning
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Solution Overview
Problem
Current data compression techniques do not support computations on compressed data, limiting their applicability in scenarios requiring privacy-preserving processing or direct operations on compressed representations.
Innovation Solution
A codebook-based homomorphic compression technique that quantizes data, generates an optimized codebook, and performs homomorphic operations on compressed representations, enabling direct computations like addition, subtraction, and averaging without decryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional compression techniques are used, then data storage and transmission costs are reduced, but computations cannot be performed on the compressed data
Solution Approach 1:
The patent transforms the data representation parameters by converting raw data into quantized values and then into codewords from a codebook. This parameter transformation enables the compressed data to maintain mathematical properties that allow homomorphic operations, resolving the contradiction between compression and computational capability
Solution Approach 2:
The patent introduces an intermediary layer of codewords that map to both compressed representations and mathematical operations. These codewords serve as mediators that enable computations on compressed data while maintaining the ability to retrieve original information, thus allowing both compression and computation
2Adaptability or versatility
If fully homomorphic encryption is used, then computations on encrypted data are enabled, but ciphertext expansion and computational overhead increase significantly
Solution Approach 1:
The patent segments the encryption process into two distinct phases: compression with quantization followed by encryption of the compressed form. This segmentation allows the system to perform computations on the compressed encrypted data with reduced overhead compared to encrypting the full original data, while still maintaining privacy-preserving capabilities
Solution Approach 2:
The patent applies partial encryption by encrypting only the compressed representation rather than the full original data. This partial action reduces the computational overhead and ciphertext expansion associated with fully homomorphic encryption while still enabling privacy-preserving computations on the essential data characteristics
3Quantity of substance
If data is compressed before encryption, then ciphertext size is reduced, but computation accuracy may deteriorate due to quantization
Solution Approach 1:
The patent applies different levels of quantization to different data characteristics, preserving local quality where it matters most for computations. By selectively maintaining precision in critical data regions while compressing less critical areas, the system achieves both size reduction and acceptable accuracy for homomorphic operations
Data Source
AI summary
The codebook-based homomorphic compression system is a novel approach that combines data compression and homomorphic encryption to enable efficient and secure computation on compressed data. It involves quantizing the input data, generating an optimized codebook using techniques like Huffman coding or deep learning, and compressing the data by replacing each value with its corresponding codeword. The compressed data is then encrypted using a homomorphic encryption scheme, allowing computations to be performed directly on the encrypted compressed data without decryption. The system includes a deep learning architecture trained on and operating on the homomorphically compressed and encrypted data, enabling inference and decision-making while maintaining data privacy. This approach combines the benefits of data compression, homomorphic encryption, and deep learning, enabling efficient storage, transmission, and secure computation on compressed data streams.