Trust Module for Privacy-Preserving Data Transformation
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Solution Overview
Problem
Existing database systems face challenges in balancing user data privacy with the need for administrators to analyze user data for service improvement, as restrictive privacy policies hinder data-driven innovation while unregulated data analysis compromises user privacy.
Innovation Solution
A service system with a trust module that applies user-defined policies to transform user data, ensuring privacy by masking sensitive information while providing administrators with comparative insights, such as normalized sales data, to facilitate service enhancements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entities analyze user data to improve software, then service quality is improved, but user privacy is compromised
Solution Approach 1:
The patent introduces a trust module as an intermediary between the data storage system and administrators. This module receives user data, applies transformation rules defined by user policies, and returns transformed data to administrators. The trust module acts as a mediator that enables data analysis while protecting user privacy through policy-based transformations.
Solution Approach 2:
The patent transforms user data by changing its parameters according to user-defined policies. For example, data may be anonymized, aggregated, or modified in ways that preserve analytical value while removing personally identifiable information. The transformation rules allow the same data to serve both privacy protection and analysis purposes.
2Object-affected harmful factors
If entities implement restrictive privacy policies, then user privacy is protected, but data-driven innovation is hindered
Solution Approach 1:
The trust module serves as an intermediary that enables administrators to access transformed user data while respecting privacy policies. Administrators receive data that has been processed according to user-defined transformations, allowing them to perform analysis and improve services without violating privacy constraints.
Solution Approach 2:
The system dynamically applies different transformation rules based on user policies. The trust module can transform data in different ways depending on the specific privacy requirements and analysis needs, allowing flexible adaptation between privacy protection and data availability for innovation.
3Measurement precision
If administrators access raw user data, then analysis accuracy is improved, but user privacy is compromised
Solution Approach 1:
The trust module is positioned between the data storage system and administrators, transforming data before it reaches administrators. This intermediary ensures that administrators receive data suitable for analysis while privacy protections are maintained through policy-enforced transformations.
Solution Approach 2:
The system changes data parameters through transformation rules that preserve analytical characteristics while removing sensitive information. The transformations maintain the structural and relational properties needed for accurate analysis while modifying individual data points to protect privacy.
Data Source
AI summary
A service system provides users with access to online services. As part of providing the services to users, the service system stores data for users in a data storage system. When the service system receives a request from an administrator of the system for user data stored in the data storage system, the service system identifies the data requested by the administrator. The service system also determines policies that apply to the data. The determined policies indicate transformations that are to be performed on the data to protect the privacy of the users. The service system transforms the user data as indicated by the policies and provides the transformed user data to the administrator.