Information Processing System for Privacy-Preserving Data Sharing
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Solution Overview
Problem
Companies are reluctant to share customer data due to concerns about personal information protection and the high value of granular data, limiting its effective utilization in society.
Innovation Solution
An information-processing system that combines feature data from multiple databases to create virtual constituents, increasing data granularity while protecting sensitive information, thereby reducing barriers to data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If detailed customer data is provided outside the company, then data utilization effectiveness is improved, but personal information protection is compromised
Solution Approach 1:
The patent segments customer data into two distinct layers: identification information (personal identifiers) and feature data (behavioral characteristics). By separating these elements, the system enables external utilization of feature data while preventing exposure of sensitive personal information, thus resolving the contradiction between data utility and privacy protection.
Solution Approach 2:
The patent introduces a data management system that acts as an intermediary between data holders and data users. This intermediary processes and manages the provision of feature data, enabling effective data utilization while implementing privacy protection measures, thereby mediating between the conflicting needs of data sharing and personal information security.
2Loss of information
If granular customer data is shared, then information value is increased, but disadvantages to data holders increase
Solution Approach 1:
The patent extracts feature data from complete customer records, separating behavioral characteristics from identifying information. This extraction process preserves the high information value of granular data for analytical purposes while removing the harmful elements (personal identifiers) that could disadvantage data holders through privacy breaches or competitive harm.
Solution Approach 2:
The patent transforms data parameters by converting detailed personal information into aggregated or anonymized feature representations. This parameter transformation maintains the analytical utility and information value of the data while changing its form to eliminate disadvantages to data holders, such as loss of competitive advantage or privacy risks.
3Object-affected harmful factors
If encryption and information removal are applied to protect privacy, then personal information protection is improved, but data granularity is reduced
Solution Approach 1:
Rather than applying encryption or removal that reduces granularity, the patent segments data into identification information and feature data. This segmentation preserves the full granularity of behavioral feature data while protecting personal information, avoiding the trade-off present in traditional encryption approaches.
Data Source
AI summary
The information processing system comprises a storage unit and a combining unit. The combining unit is configured to store first and second databases stored by the storage unit. The first database is provided with feature data of each virtual constituent, the feature data being generated by integrating feature data of a plurality of constituents identical or similar in feature based on the feature data of each constituent of a first group. The second database is provided with feature data of each constituent of a second group. Each of the feature data provided in the first and second databases, include reference data that represents a common type of feature. The combining unit combines the first database and the second database so as to combine the feature data identical or similar in feature represented by the reference data between the first database and the second database.