Homomorphic Codeword Models for Privacy-Preserving Deep Learning
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Solution Overview
Problem
Current deep learning approaches face challenges in efficiently processing and analyzing large volumes of data while maintaining privacy and security, particularly in sensitive domains like healthcare and finance, where data confidentiality is crucial.
Innovation Solution
A system and method for deep learning using a large codeword model with homomorphically compressed and dyadically encrypted data, incorporating preprocessing, compression, encryption, and tokenization, utilizing a machine learning core that can be either a conventional transformer-based architecture or a latent transformer core, with additional security measures like cryptographically secure random numbers and a neural upsampler for enhanced security and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is processed in plaintext for deep learning, then processing efficiency and model accuracy are improved, but data privacy and security are compromised
Solution Approach 1:
The system applies homomorphic encryption to data before processing, transforming plaintext into ciphertext in advance. This preliminary encryption action allows the data to be processed in encrypted form, maintaining security while enabling subsequent deep learning operations without decryption.
Solution Approach 2:
Homomorphic encryption serves as an intermediary mechanism between the data and the processing system. It enables computations to be performed on encrypted data through mathematical transformations, acting as a mediator that preserves privacy while allowing productive processing through special homomorphic operations.
2Reliability
If data is encrypted before processing, then data privacy and security are maintained, but processing complexity and computational overhead increase
Solution Approach 1:
The system replaces traditional mechanical decryption-then-processing workflows with homomorphic cryptographic operations. Instead of decrypting data and processing it through conventional neural networks, the system uses homomorphic encryption to enable direct computation on ciphertext, substituting complex security management with mathematical operations.
Solution Approach 2:
The system changes the state parameter of data from plaintext to ciphertext, fundamentally altering how data is handled throughout the processing pipeline. This parameter change enables security maintenance while allowing processing through homomorphic transformations that operate on encrypted representations.
3Reliability
If traditional encryption methods are used, then data security is improved, but the ability to perform computations on encrypted data without decryption is lost
Solution Approach 1:
The system substitutes traditional encryption mechanisms with homomorphic encryption, which replaces the need for decryption-with-computation-reencryption cycles with direct homomorphic operations. This substitution enables computations to be performed naturally on encrypted data through mathematical properties of the homomorphic scheme.
Solution Approach 2:
The system changes the cryptographic parameter from standard encryption to homomorphic encryption, which fundamentally alters the data state to enable computational operations. This parameter change transforms encrypted data into a form that supports arithmetic and logical operations while maintaining security, enabling ease of computation without decryption.
Data Source
AI summary
A system and method for deep learning using a large codeword model with homomorphically compressed and dyadically encrypted data is disclosed. The system preprocesses input data, applies homomorphic-dyadic compression and encryption, tokenizes the compressed data into sourceblocks, and assigns codewords using a codebook. These codewords are processed through a machine learning core, which can be either a conventional transformer-based architecture or a latent transformer core utilizing a variational autoencoder. The system enables secure operations on encrypted data, preserving privacy while allowing complex computations. The processed output is decrypted, decompressed, and translated to match the input modality. A neural upsampler may further enhance the output. The machine learning core is continuously trained using the processed data and additional training data, improving performance over time.