Latent Space Compression for Privacy-Preserving Homomorphic Processing
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Solution Overview
Problem
Current deep learning approaches for data compression lack efficient methods for homomorphic compression and decompression, particularly in maintaining data privacy and security, especially in handling sensitive information like medical and financial records.
Innovation Solution
The system employs a variational autoencoder with a latent space that enables homomorphic operations, allowing for data compression and decompression without decrypting encrypted data, using neural upsampling and correlation networks to reconstruct original data while preserving privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is compressed using traditional deep learning approaches, then storage and transmission requirements are reduced, but data privacy and security are compromised
Solution Approach 1:
The patent introduces a homomorphic encryption layer as an intermediary between the data and the compression algorithm. This allows the data to be compressed while remaining encrypted, so that the compression algorithm operates on ciphertext without needing to decrypt it first. The intermediary encryption layer preserves data privacy while enabling compression functionality.
Solution Approach 2:
The patent replaces traditional mechanical decryption-compression-encryption workflows with a homomorphic encryption system where mathematical operations can be performed directly on encrypted data. This substitution eliminates the need to decrypt data for compression, maintaining security throughout the process.
2Reliability
If data is encrypted to maintain privacy, then data security is improved, but computational efficiency and processing speed deteriorate
Solution Approach 1:
The patent applies homomorphic encryption to the data before compression in advance, so that subsequent compression operations can be performed on the already-encrypted data without requiring additional decryption steps. This preliminary encryption action enables efficient processing of secure data throughout the pipeline.
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.