Information processing method, information processing program, and information processing system
A secure information processing system with TEE and key management ensures confidential data sharing for improved machine learning accuracy by encrypting and processing data within a trusted environment, addressing security concerns and enhancing predictive capabilities.
JP7762929B1Active Publication Date: 2025-10-31EAGLYS INC
8 Cites 0 Cited by
Patent Information
- Application Number
- JP2025125260
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- Filing Date
- 2025-07-26
- Publication Date
- 2025-10-31
- Estimated Expiration
- 2045-07-26
AI Technical Summary
Technical Problem
The challenge of sharing highly confidential materials data among companies for improved machine learning models is hindered by security concerns such as information leaks and unauthorized use.
Method used
A secure information processing system with a trusted execution environment (TEE) and key management system ensures data confidentiality by encrypting and processing sensitive data within a secure area, using Diffie-Hellman key agreement for secure data sharing and model usage, without disclosing raw data contents to other organizations.
Benefits of technology
Enables high-accuracy machine learning predictions by combining data from multiple companies while maintaining strict security, preventing data leaks and unauthorized use.
✦ Generated by Eureka AI based on patent content.
Abstract
The use of data held by each organization without disclosing the contents to others. [Solution] A method for use in a system including a plurality of user terminals each having a control unit and a memory unit for storing sensitive data including a plurality of records, a data processing device having a calculation unit in a secure area, a key management device for managing a system key, and a memory device for storing a machine learning model encrypted using the system key, the method comprising: aggregating data held by the plurality of user terminals in the secure area to create combined data; training a machine learning model using the combined data to create a model with higher predictive accuracy than when training is performed using only the user's own data; and, by using the machine learning model in the secure area to perform inference based on the combined data, predictions can be made with higher accuracy than when training is performed using only the user's own data.
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