Privacy Utility Trade Off Tool for Data Sharing
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Solution Overview
Problem
Existing methods for sharing private data struggle to achieve an optimal balance between preserving user privacy and maintaining data utility, often requiring subjective and repetitive modifications that are ambiguous and difficult to quantify, leading to potential privacy breaches and utility loss.
Innovation Solution
A computer-implemented method that quantifies the privacy content of private data based on its uniqueness and determines an optimal privacy-utility trade-off point model using analytical analysis, considering the privacy requirements of users and the utility requirements of third parties, to provide precise and objective privacy settings for data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If subjective censorship methods are used to remove sensitive attributes, then ease of operation is improved, but reliability of privacy protection deteriorates
Solution Approach 1:
The patent replaces subjective manual censorship with an automated analytical system that uses algorithms to objectively identify and protect privacy-sensitive information. The system automatically analyzes data attributes, determines privacy risks, and applies protection measures without relying on human judgment, thereby improving both operational efficiency and privacy protection reliability.
Solution Approach 2:
The patent introduces an intermediary analytical system that acts as a mediator between data sharing needs and privacy protection requirements. This system analyzes the relationship between data attributes and potential privacy risks, providing an objective assessment that balances utility with privacy protection, resolving the contradiction between ease of sharing and reliability of protection.
2Productivity
If more attributes are shared to maintain data utility, then productivity is improved, but loss of information increases
Solution Approach 1:
The patent segments data attributes into different categories based on their privacy sensitivity and utility value. By dividing the data into segments with different protection levels, the system can share non-sensitive attributes freely to maintain productivity while applying protective measures to sensitive attributes, thereby minimizing privacy information loss while preserving data utility.
Solution Approach 2:
The patent applies different quality levels of protection to different parts of the data based on local privacy risks. Rather than uniformly protecting or sharing all data, the system analyzes each attribute's specific privacy implications and applies appropriate protection measures only where necessary, maintaining data utility in non-sensitive areas while protecting privacy in sensitive areas.
3Measurement precision
If analytical analysis is performed to determine optimal trade-off points, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a self-service analytical system that automatically performs privacy-utility trade-off analysis without requiring external intervention. The system self-evaluates data attributes, determines privacy risks, and identifies optimal sharing points autonomously, achieving high measurement precision while managing complexity through automation rather than manual processes.
Data Source
AI summary
Method(s) and system(s) for providing an optimal trade off point between privacy of a private data and utility of a utility application thereof are described. The method includes quantifying privacy content of a private data associated with a user based on uniqueness of information in the private data, where the private content comprises sensitive information about the user. The method further includes determining a privacy-utility trade off point model based on analytical analysis of the privacy content, a privacy requirement of the user, and a utility requirement of third party to which the private data is disclosed, where the privacy-utility trade off point model is indicative of optimal private data sharing technique with the third party. Furthermore, the method also includes identifying privacy settings for the user based on risk appetite of the third party, utilizing the determined privacy-utility tradeoff point model.