Multimodal VAE Latent Space for Privacy-Preserving Data Processing
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Solution Overview
Problem
Existing approaches have not fully leveraged the potential of combining variational autoencoders with homomorphic operations in the context of multi-modal data processing, particularly when handling diverse data types simultaneously, leading to challenges in maintaining data privacy, computational efficiency, and effective synchronization and alignment of different data types.
Innovation Solution
A system and method for compressing and restoring multi-modal data using a multi-modal variational autoencoder that incorporates modality-specific and shared layers, activation functions, and cross-modal attention mechanisms, enabling homomorphic operations in a unified latent space to preserve data privacy and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional autoencoders are used for multi-modal data compression, then data processing capability is improved, but data privacy protection deteriorates
Solution Approach 1:
The patent introduces a homomorphic encryption layer as an intermediary between the autoencoder and the data. The encoder processes encrypted data through homomorphic operations in the latent space, allowing computation on encrypted representations without decryption. This mediator enables both data processing and privacy protection simultaneously.
Solution Approach 2:
The patent transforms the latent space representation to support homomorphic operations. By changing the parameterization of the latent space (using additive and scalar multiplication compatible representations), the system enables mathematical operations on encrypted data while maintaining the autoencoder's compression and reconstruction capabilities.
2Object-affected harmful factors
If homomorphic operations are performed on compressed data, then data privacy is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and isolates the homomorphic operations to specific layers and operations within the autoencoder architecture. By concentrating homomorphic operations in the latent space transformation rather than throughout the entire network, the computational complexity is localized and managed more efficiently.
Solution Approach 2:
The patent designs the latent space to serve multiple functions: it maintains the compression capability of traditional autoencoders while simultaneously supporting homomorphic operations for encrypted data processing. This universal latent space representation handles both decryption-resistant computation and efficient data reconstruction.
3Adaptability or versatility
If multi-modal data is processed simultaneously, then data integration capability is improved, but synchronization and alignment difficulty increases
Solution Approach 1:
The patent segments the multi-modal data processing into modality-specific encoder branches that independently process each data type (image, text, audio, etc.). Each branch transforms its input into a standardized latent representation, avoiding the need to directly synchronize and align raw multi-modal data while maintaining integration capability through the unified latent space.
Data Source
AI summary
A system and method for processing multi-modal data using variational autoencoders with homomorphic operations is disclosed. Multi-modal input data, comprising a plurality of different data types, is encoded into a unified latent space using a multi-modal variational autoencoder. The system performs homomorphic operations on the encoded data within the unified latent space while preserving mathematical relationships between data types. The processed data is then decoded using the multi-modal variational autoencoder to generate reconstructed output. The system implements modality-specific processing layers and cross-modal mechanisms to handle diverse data types effectively. The homomorphic operations enable computations to be performed while the data is in encoded form, maintaining the validity of these operations in the decoded output. This approach provides a comprehensive framework for processing multi-modal data efficiently while preserving privacy and data integrity across different modalities.