Automated AI Model Conversion for Secure Computation Formats
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Solution Overview
Problem
Conventional data analysis of sensitive data requiring confidentiality in machine learning requires specialized knowledge to convert learning models into formats suitable for secure computation, leading to inefficiencies in operation.
Innovation Solution
A conversion device and system that automatically converts learning models generated by AI libraries into input formats applicable to secure computation libraries, utilizing an acquisition unit, storage unit, and conversion processing unit to handle secure computation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional manual conversion methods are used to convert learning models to secure computation formats, then data security is maintained, but operation efficiency deteriorates due to requiring specialized knowledge and time-consuming conversion processes
Solution Approach 1:
The conversion device automatically converts learning models to secure computation formats without requiring manual intervention or specialized knowledge. The system self-performs the conversion process by acquiring the learning model, retrieving corresponding conversion parameters, and executing the conversion automatically, thereby eliminating the need for human expertise while maintaining security
Solution Approach 2:
The conversion parameters and correspondence tables are pre-stored in the storage unit before actual conversion is needed. This preliminary preparation allows the conversion device to quickly retrieve and apply the appropriate conversion parameters without time-consuming manual configuration, significantly improving operation efficiency while maintaining data security
2Manufacturing precision
If specialized knowledge is required for converting learning models to secure computation formats, then conversion accuracy is maintained, but ease of operation deteriorates due to complex conversion procedures
Solution Approach 1:
The correspondence table stored in the storage unit acts as an intermediary that maps learning model parameters to secure computation format parameters. This intermediary structure enables automatic and accurate conversion without requiring users to understand the complex relationships between different formats, thereby improving ease of operation while maintaining conversion accuracy
Solution Approach 2:
The conversion device performs the conversion process automatically without requiring user expertise. The system self-determines the appropriate conversion parameters by retrieving them from the correspondence table and executes the conversion autonomously, making the system easy to operate while ensuring accurate conversion through programmed logic
3Adaptability or versatility
If manual conversion processes are used, then conversion flexibility can be adjusted, but loss of time increases due to time-consuming conversion operations
Solution Approach 1:
The conversion parameters and correspondence tables are pre-computed and stored in the storage unit before conversion is needed. This preliminary action eliminates the need for time-consuming manual conversion processes, reducing conversion time while maintaining flexibility through the pre-stored parameter sets that can be retrieved and applied automatically
Solution Approach 2:
The manual mechanical conversion process is replaced with an automated computer-based conversion system. The conversion device uses software algorithms to automatically transform learning models to secure computation formats, significantly reducing conversion time while maintaining flexibility through programmable conversion parameters that can be adjusted as needed
Data Source
AI summary
A conversion device for secure computation for converting an input data which is an object data of secure computation into an input format applicable to the secure computation is provided. A conversion device for secure computation of the present invention includes an acquisition unit configured to acquire an object data of the secure computation; a storage unit configured to store a correspondence table specifying an input format required for executing the secure computation; a conversion processing unit configured to perform a conversion from the acquired object data into a secure computation data in accordance with the correspondence table; and an output unit configured to output the secure computation data.