Variational Autoencoder Latent Compression for Homomorphic Data Processing
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Solution Overview
Problem
Current deep learning approaches for data compression and restoration lack efficient methods for homomorphic operations, which are essential for secure data processing and privacy preservation, especially in handling sensitive information.
Innovation Solution
The use of variational autoencoders with a continuously differentiable latent space enables homomorphic compression and decompression, allowing computations to be performed on encrypted data without decrypting it, thereby maintaining data privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data compression methods are used, then storage requirements are reduced, but data privacy and security are compromised
Solution Approach 1:
The patent introduces an intermediary mechanism (homomorphic encryption scheme) that allows compression operations to be performed on encrypted data without decryption. The encrypted data acts as an intermediary representation that preserves privacy while enabling compression functionality, thus resolving the contradiction between storage efficiency and data security
Solution Approach 2:
The patent transforms the data representation parameter by applying homomorphic encryption to the compressed data. This parameter change allows the data to maintain both compressed size and cryptographic protection, simultaneously achieving reduced storage requirements and preserved data privacy
2Object-affected harmful factors
If data is encrypted for security, then data privacy is maintained, but computational efficiency deteriorates
Solution Approach 1:
The patent applies homomorphic encryption preliminarily to the compressed data representation before storage or transmission. This preliminary encryption action enables subsequent compression and processing operations to be performed efficiently on the already-encrypted data without requiring decryption, thus maintaining both security and computational efficiency
Solution Approach 2:
The patent replaces the traditional mechanical approach of decrypting-compressing-encrypting with a homomorphic system where compression operations directly operate on encrypted data. This substitution eliminates the computationally expensive decryption step while achieving the same functional outcome
3Quantity of substance
If compression is applied to sensitive data, then storage efficiency is improved, but data integrity and security guarantees are lost
Solution Approach 1:
The patent creates a composite data structure that combines compressed representation with homomorphic encryption properties. This composite approach integrates both compression efficiency and security guarantees into a unified data format, allowing the data to simultaneously achieve storage efficiency and maintain integrity and security guarantees through the mathematical properties of homomorphic schemes
Data Source
AI summary
Compressing and restoring data utilizing a variational autoencoder to enable homomorphic compression techniques. 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.