Personalized ML Model Transfer Using Encryption and Obfuscation
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Solution Overview
Problem
The efficient and secure transfer of personalized machine learning models and associated data is challenging due to the need for structured hosting, data abstraction, and protection against unauthorized access.
Innovation Solution
A system and method involving a transfer AI agent that identifies, organizes, abstracts, encrypts, and obfuscates personalized ML/AI models and data for secure transfer, ensuring privacy and security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If personalized ML/AI models and data are transferred without structured hosting and organization, then transfer simplicity is improved, but data security and accessibility are worsened
Solution Approach 1:
The system performs preliminary actions by automatically organizing and structuring ML models and data before transfer occurs. The transfer agent categorizes models, extracts metadata, and prepares encrypted packages in advance, ensuring security requirements are met before the actual transfer takes place.
Solution Approach 2:
The transfer agent acts as an intermediary between source and destination storage systems. It mediates the transfer process by implementing security protocols, organizing data structures, and ensuring proper authentication without requiring direct interaction between the storage systems.
2Quantity of substance
If all data from ML models is transferred without abstraction, then data completeness is improved, but transfer efficiency and security are worsened
Solution Approach 1:
The transfer agent extracts only the essential and relevant data from ML models for transfer, rather than moving entire model datasets. This extraction process identifies critical parameters, model weights, and configuration files that are necessary for model functionality while excluding redundant or sensitive information.
Solution Approach 2:
The system applies partial action by transferring a subset of data that is sufficient for model operation without transferring complete training datasets. This partial transfer approach maintains model functionality while significantly reducing transfer time and resource requirements.
3Speed
If data is transferred without encryption and obfuscation, then transfer speed is improved, but data vulnerability to attacks is worsened
Solution Approach 1:
Encryption and obfuscation are applied in advance during the data preparation phase, before the actual transfer occurs. This preliminary security processing ensures that data is protected during transmission without requiring additional processing time during the transfer itself.
Solution Approach 2:
The system creates encrypted copies of sensitive data for transfer while maintaining the original data structure and format. This copying approach allows the transferred data to be secure while preserving model functionality, as the encrypted copies can be decrypted at the destination using proper authentication.
Data Source
AI summary
The present invention discloses a method for transferring data from one storage to another storage. The method includes identifying one or more Machine Learning (ML)/Artificial Intelligence (AI) models and data associated with the one or more ML/AI models to be transferred to the other storage selected by the transfer AI agent. The method includes organizing the one or more ML/AI models and data to be transferred to the other storage. The method includes abstracting relevant information from the one or more ML/AI models and the data. The relevant information is encrypted. The method includes applying one or more obfuscation techniques on the encrypted relevant information. The encrypted relevant information is transferred to the other storage.