Multimodal VAE Latent Compression for Privacy-Preserving Analytics
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Solution Overview
Problem
Existing deep learning systems struggle to efficiently compress and reconstruct multi-modal data, particularly in scenarios requiring data privacy and security, as they often require decryption and lack efficient methods for handling diverse data types simultaneously.
Innovation Solution
A system utilizing a multi-modal variational autoencoder with modality-specific and shared layers, capable of performing homomorphic operations in a unified latent space, allowing secure compression and reconstruction of multi-modal data without full decryption, leveraging cross-modal attention mechanisms and correlation networks for enhanced data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional autoencoders are used for data compression, then compression capability is achieved, but data privacy and security are compromised due to required decryption
Solution Approach 1:
The patent introduces homomorphic encryption as an intermediary layer that enables the autoencoder to process encrypted data directly. The encoder and decoder operate on ciphertext without decryption, using homomorphic operations that preserve mathematical relationships. This mediator allows the system to maintain data privacy while achieving compression, as the encrypted representations can still be transformed and reconstructed without exposing the original plaintext data.
2Adaptability or versatility
If multi-modal autoencoders are used to handle diverse data types, then versatility is improved, but system complexity increases
Solution Approach 1:
The patent segments the multi-modal processing system into distinct modality-specific encoder modules, each designed to handle particular data types (e.g., images, text, audio). These modular encoders process their respective modalities independently and then integrate their outputs in a unified latent space. This segmentation allows the system to maintain versatility across multiple data types while managing complexity through organized, specialized components rather than a monolithic structure.
3Reliability
If deep learning models process encrypted data using homomorphic operations, then data security is maintained, but computational overhead increases
Solution Approach 1:
The patent applies partial homomorphic decryption at strategic points in the architecture rather than maintaining full encryption throughout the entire processing pipeline. Specifically, limited decryption operations are performed only where necessary for specific processing tasks, while the majority of the compression and reconstruction operations remain in the encrypted domain. This partial approach reduces the overall computational overhead compared to fully homomorphic processing while still preserving data security for the critical encrypted portions.
Data Source
AI summary
A system and method for compressing and restoring multi-modal data utilizing a variational autoencoder to enable homomorphic compression techniques is disclosed. Multi-modal input data, comprising at least two different data types, is compressed into a unified latent space using an encoder network of a multi-modal variational autoencoder. Homomorphic operations are performed on compressed data in the latent space. The latent space compressed data is decompressed using a decoder network of the multi-modal variational autoencoder. The system utilizes modality-specific layers and cross-modal attention mechanisms to effectively process diverse data types. The homomorphic operations enable performing computations while the data is in a compressed form, preserving results of those operations in the decompressed output. This approach allows for efficient storage, transmission, and analysis of multi-modal data while maintaining privacy and data integrity across different modalities.