Information Processing System for Secure Machine Learning
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Solution Overview
Problem
Existing machine learning methods face a decline in learning effectiveness due to the exclusion of business confidential information during data analysis, which compromises the accuracy of predictions.
Innovation Solution
An information processing system comprising two interconnected systems: one transforms data to remove specific information, allowing the other to perform machine learning without preserving that information, thereby generating a learning deliverable that reflects the original data's specifics without exposing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If specific information is excluded from training data to preserve business confidential information, then information security is improved, but learning effectiveness of machine learning deteriorates
Solution Approach 1:
The system is divided into two distinct systems: a first system that holds and processes the original data including specific information, and a second system that performs machine learning using transformed data without specific information. This segmentation allows each system to operate with appropriate data for its function, resolving the contradiction between security and learning effectiveness.
Solution Approach 2:
A transformation unit acts as an intermediary between the first system and the second system. It transforms the original data into transformed data that preserves learning value while removing specific information, enabling the second system to perform effective machine learning without accessing sensitive data.
2Reliability
If transformed data without specific information is used for machine learning, then information security is improved, but prediction accuracy deteriorates
Solution Approach 1:
The transformation unit performs preliminary transformation of the data before it reaches the machine learning system. By removing specific information in advance while preserving other characteristics, the system ensures that the learning process works with secure data that still maintains predictive value.
Solution Approach 2:
The transformation unit changes the parameters of the data by removing specific information while preserving other characteristics. This parameter modification allows the data to maintain its utility for machine learning while eliminating security risks associated with specific information.
3Measurement precision
If original data with specific information is used for machine learning, then learning effectiveness is improved, but information security deteriorates
Solution Approach 1:
The transformation unit extracts and removes specific information from the original data while preserving other useful characteristics. This extraction process creates transformed data that maintains learning effectiveness while eliminating security risks.
Solution Approach 2:
The system creates a transformed copy of the original data that preserves the essential learning characteristics while removing specific information. This copy can be safely used for machine learning without compromising the security of the original data.
Data Source
AI summary
An information processing system includes a first system and a second system which are ready to communicate with each other. The first system transforms first data including specific information into second data not including the specific information and outputs the second data to the second system. The second system performs machine learning using the second data to generate a second learning deliverable and outputs the second learning deliverable to the first system. The first system obtains, based on at least a part of the first data and the second learning deliverable provided by the second system, the first learning deliverable.