Variational Autoencoder Compression for Homomorphic Data Processing
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Solution Overview
Problem
Current deep learning methods for data compression and restoration lack efficient mechanisms for handling sensitive data, particularly in areas requiring privacy and security, such as medical and financial records, where homomorphic operations are not effectively supported.
Innovation Solution
The implementation of variational autoencoders with convolutional layers, pooling layers, and activation functions enables homomorphic compression and decompression, allowing for continuous differentiable latent spaces that facilitate operations on encrypted data without decryption, utilizing techniques like addition, subtraction, and scalar multiplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression methods are used, then storage and transmission efficiency is improved, but security and privacy protection of sensitive data deteriorates
Solution Approach 1:
The patent introduces an autoencoder model as an intermediary that performs compression in an encrypted latent space. The encoder transforms plaintext data into encrypted latent representations, and the decoder reconstructs the original data from these encrypted forms. This intermediary mechanism enables compression without exposing the actual data content, thus improving storage efficiency while maintaining security.
Solution Approach 2:
The patent changes the parameter space by transforming data from its original plaintext form into a latent space representation through the autoencoder. This parameter transformation allows the data to be compressed and stored in an encrypted form that maintains its essential characteristics while protecting the original information, resolving the contradiction between compression and security.
2Reliability
If data is encrypted for security, then privacy protection is improved, but computational efficiency and processing speed deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the autoencoder model on plaintext data to learn the optimal compression and reconstruction mappings. Once trained, the model can efficiently compress and decompress data in encrypted form without requiring complex cryptographic operations for each compression task, thus improving computational efficiency while maintaining security.
3Quantity of substance
If compression ratio is increased, then storage efficiency is improved, but data reconstruction quality and information fidelity deteriorates
Solution Approach 1:
The patent implements feedback through the loss function that measures the difference between the original input data and the reconstructed output data. During training, this feedback signal guides the autoencoder to minimize reconstruction error, ensuring that even at high compression ratios, the essential features and information are preserved in the latent representation, thus maintaining reconstruction quality.
Data Source
AI summary
A system and method for compressing and restoring data utilizing a variational autoencoder to enable homomorphic compression techniques is disclosed. Input data is compressed into a latent space using an encoder network of a variational autoencoder. Homomorphic operations are performed on the compressed data in the latent space. The latent space compressed data is decompressed using a decoder network of the variational autoencoder. The homomorphic operations can enable performing operations while the data is in a compressed form, and preserving results of those operations while the data is in a decompressed form.