Concealment Record Set Generation for Secure Data Analysis
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Solution Overview
Problem
When confidential data, such as manufacturing data, is concealed for analysis using a learning model, the inability to differentiate individual records hinders appropriate data analysis, as the concealment prevents the third-party analyst from performing effective analysis.
Innovation Solution
An information processing system that generates a concealment record set and identification information by converting attribute names and values, allowing the second information processing apparatus to analyze the records while maintaining record uniqueness, thereby enabling effective analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidential data is concealed for analysis, then information security is improved, but data analysis effectiveness deteriorates
Solution Approach 1:
The patent segments confidential data into multiple concealed versions, each with different concealment levels. By dividing the data into segments that can be independently analyzed, the system maintains security while preserving analytical utility. The segmentation allows analysts to work with specific portions of data without accessing the complete confidential dataset.
Solution Approach 2:
The patent introduces an intermediary concealment management system that mediates between the confidential data and the analysis system. This intermediary layer transforms confidential data into concealed data that retains analytical value while removing sensitive information. The intermediary manages the conversion process and ensures that analysis can proceed on concealed data without direct access to original confidential data.
2Measurement precision
If individual records are differentiated for analysis, then data analysis effectiveness is improved, but information leakage risk increases
Solution Approach 1:
The patent applies local quality by implementing different concealment strategies for different parts of the data. Sensitive attributes are concealed or generalized, while non-sensitive attributes retain their original detail. This allows the system to maintain record differentiation for analysis purposes while protecting specific sensitive information elements from leakage.
Solution Approach 2:
The patent changes data parameters by transforming confidential data into concealed data with modified attributes. The transformation alters the representation of sensitive information while preserving the structural and relational properties needed for analysis. Parameter changes include generalization, aggregation, and pseudonymization that maintain analytical utility while reducing information leakage risk.
Data Source
AI summary
A first information processing apparatus includes a record set acquiring unit that acquires a record set, a record identification information acquiring unit that acquires record identification information, a concealing unit that generates a concealment record set from the record set using conversion information and generates concealment record identification information based on the record identification information and the conversion information, and a first transmitter that transmits the concealment record set and the concealment record identification information to a second information processing apparatus. The second information processing apparatus includes a second receiver that receives the concealment record set and the concealment record identification information from the first information processing apparatus and a data analyzer that analyzes each record in the concealment record set identified based on the concealment record identification information using a learning model or a numerical model.