Large Codeword Deep Learning on Homomorphically Compressed Data
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Solution Overview
Problem
Existing deep learning systems struggle to efficiently process and analyze large volumes of data while maintaining privacy and security, particularly in sensitive domains like healthcare and finance.
Innovation Solution
A system and method for deep learning using a large codeword model with homomorphically compressed data, incorporating homomorphic compression, dyadic encryption, and transformer architectures to process and analyze data securely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is processed in plaintext to enable efficient deep learning operations, then processing speed and model training efficiency are improved, but data privacy and security are compromised
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary layer that allows deep learning models to process encrypted data without decryption. The encrypted data serves as a mediator between the raw sensitive data and the model, enabling computations to be performed on ciphertext while maintaining data privacy. This resolves the contradiction by allowing efficient processing (through optimized homomorphic operations) while preserving security (through encryption).
Solution Approach 2:
The patent transforms data from plaintext to ciphertext form, changing its state parameter. This parameter change enables the data to simultaneously support efficient homomorphic operations while maintaining security properties. The encrypted representation allows mathematical operations to be performed that preserve the underlying data relationships needed for deep learning while preventing direct access to sensitive information.
2Object-affected harmful factors
If homomorphic encryption is applied to data before processing, then data security and privacy are maintained, but computational complexity and processing time increase
Solution Approach 1:
The patent applies homomorphic encryption to data before it enters the deep learning pipeline, performing the encryption action in advance. This preliminary action ensures security is built into the system architecture rather than added as a post-processing layer, allowing subsequent operations to work with pre-encrypted data efficiently.
Solution Approach 2:
The patent replaces traditional plaintext processing mechanisms with homomorphic encrypted processing mechanisms. Instead of decrypting data for processing and then re-encrypting results, the system substitutes entire plaintext computation pipelines with homomorphic encryption-based operations, eliminating repeated encryption/decryption cycles and reducing overall computational overhead.
3Measurement precision
If large volumes of data are processed to improve model accuracy, then deep learning performance is enhanced, but data transmission and storage requirements increase
Solution Approach 1:
The patent extracts and processes only the essential features and patterns from large volumes of encrypted data through homomorphic operations, rather than processing all raw data. The deep learning model learns to identify and process key discriminative features while ignoring redundant information, effectively extracting the necessary signal from large data volumes while reducing transmission and storage requirements.
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.