Multi-Modal VAE Compression for Homomorphic Latent-Space Processing
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Solution Overview
Problem
Existing deep learning technologies face challenges in efficiently compressing and restoring multi-modal data, particularly in handling diverse data types simultaneously while ensuring data privacy and security.
Innovation Solution
The system employs a multi-modal variational autoencoder with modality-specific and shared layers, activation functions, and cross-modal attention mechanisms to perform homomorphic compression and decompression in a unified latent space, enabling operations on encrypted data without decryption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional autoencoders are used for data compression, then compression capability is achieved, but they can only handle single modality data and cannot process diverse data types simultaneously
Solution Approach 1:
The system segments the encoding process into modality-specific encoders that process different data types (images, audio, text, sensor data) separately, then combines their outputs in a shared latent space. This segmentation allows each encoder to be optimized for its specific modality while maintaining overall system versatility.
Solution Approach 2:
The patent implements a universal latent space that receives encoded representations from multiple modality-specific encoders. This shared latent space serves as a common interface for processing diverse data types, enabling the system to handle multiple modalities simultaneously through a unified architecture.
2Adaptability or versatility
If multi-modal autoencoders are used to handle diverse data types, then adaptability improves, but computational complexity and processing overhead increase
Solution Approach 1:
The system extracts essential features from each modality through modality-specific encoders and represents them in a compact latent space. By extracting only the most important features rather than processing complete raw data, the system reduces computational overhead while maintaining the ability to handle diverse data types.
Solution Approach 2:
The patent transforms multi-modal data from high-dimensional raw form into a lower-dimensional unified latent space representation. This dimensionality reduction enables efficient processing of diverse data types by projecting them into a common space where computations are more tractable and energy-efficient.
3Reliability
If data is compressed and encrypted for privacy protection, then data security is improved, but computational operations require decryption which increases processing time
Solution Approach 1:
The system performs homomorphic compression that preserves the mathematical structure of encrypted data in advance. By pre-processing data through the encoding pipeline while it remains encrypted, the system prepares compressed representations that can be operated on directly without requiring decryption, thus reducing processing time for subsequent operations.
Solution Approach 2:
The patent introduces a homomorphic latent space as an intermediary representation that maintains the encrypted nature of data while enabling computational operations. This intermediary space allows processing to occur on encrypted compressed data without breaking cryptographic protection, eliminating the need for time-consuming decryption steps.
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.