Privacy Sensitivity Measures for Data Item Combinations
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Solution Overview
Problem
Users face difficulties in evaluating and understanding the privacy risks associated with revealing personal data, often weighing trust and short-term benefits against complex long-term privacy risks, leading to potential underestimation of privacy implications.
Innovation Solution
A method and apparatus for determining and communicating privacy sensitivity measures to users by identifying data item combinations, calculating the number of distinct users contributing to these combinations, and using these measures to protect user privacy and provide personalized privacy recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users reveal personal information to receive services or improve service quality, then service personalization and quality improve, but privacy risk increases
Solution Approach 1:
The system provides feedback to users about the privacy sensitivity of their data combinations by calculating and communicating privacy sensitivity measures. This feedback loop enables users to understand the privacy implications of data sharing and make informed decisions about what personal information to reveal for service personalization.
Solution Approach 2:
The patent introduces an intermediary privacy evaluation system that mediates between users and services. This intermediary calculates privacy sensitivity measures for data combinations and communicates them to users, serving as a bridge that helps users assess privacy risks before sharing personal information.
2Loss of information
If users are provided with detailed privacy risk information, then user awareness and informed decision-making improve, but system complexity increases
Solution Approach 1:
The patent segments the privacy evaluation process into distinct components: receiving user data, identifying data item combinations, determining privacy sensitivity measures for each combination, and communicating results to users. This segmentation makes the complex privacy evaluation task manageable and systematic.
Solution Approach 2:
The system performs automated privacy sensitivity calculations and communications without requiring manual user analysis. The automated identification of data combinations and calculation of privacy measures reduces the complexity burden on users while providing comprehensive privacy information.
3Measurement precision
If the system evaluates all possible data item combinations for privacy sensitivity, then privacy assessment accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary identification of data item combinations before calculating privacy sensitivity measures. By pre-organizing and identifying relevant data combinations, the system reduces the computational burden of subsequent privacy sensitivity calculations while maintaining comprehensive assessment accuracy.
Data Source
AI summary
The invention relates to receiving data originating from multiple users, identifying data item combinations occurring within said data, determining privacy sensitivity measures to said data item combinations, and communicating privacy sensitivity measure(s) to user(s) concerned. The privacy sensitivity measures can be used to protect user privacy.