Privacy-Preserving Data Revelation System with Noise Parameter
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Solution Overview
Problem
Conventional systems fail to address the privacy-accuracy trade-off effectively, prioritizing third-party utility over user privacy and lacking a scalable, operationally meaningful method for privacy-adjusted data revelation, which is essential for balancing user data utility and privacy in a way that aligns with user preferences.
Innovation Solution
A system that formalizes the privacy-accuracy trade-off in a mathematical framework without separating user data into private and public components, using a single parameter to capture user privacy preferences and providing an optimal data revelation method that ensures user privacy while maximizing accuracy for the third party, through the extraction, noise estimation, and stochastic mapping of valuable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If more user data is revealed to a third party, then the utility of the service increases, but the user's privacy is compromised
Solution Approach 1:
The patent introduces a privacy parameter β that allows the user to control the trade-off between privacy and utility. By changing this parameter, the system adjusts the amount of noise added to the data revelation, thereby controlling the balance between service utility and privacy protection without requiring explicit data separation or adversarial models.
Solution Approach 2:
The patent uses noise as an intermediary mechanism to mediate between the user's data and the third party's utility needs. The noise acts as a buffer that allows partial information revelation while maintaining privacy, enabling the system to achieve both utility and privacy protection simultaneously without direct data exposure.
2Object-affected harmful factors
If user data is separated into private and public components, then privacy control is improved, but the system complexity increases and scalability is reduced
Solution Approach 1:
The patent extracts the privacy control mechanism from the data structure itself and places it in the revelation process. Instead of separating data into private and public components, the system extracts only the necessary information and adds noise during the revelation process, simplifying the overall system architecture while maintaining privacy control.
Solution Approach 2:
The patent creates a universal privacy-preserving revelation mechanism that works for any type of user data without requiring data separation. The same noise-based approach can be applied to different data types and scenarios, making the system scalable and adaptable without increasing complexity.
3Object-affected harmful factors
If noise is added to protect privacy, then user privacy is improved, but the accuracy of data revelation decreases
Solution Approach 1:
The patent uses the privacy parameter β to control the amount of noise added to the data revelation. By adjusting this parameter, the system can achieve different balances between privacy protection and data accuracy, allowing the user to optimize the trade-off based on their specific needs and preferences.
Solution Approach 2:
The patent makes the noise addition process dynamic by allowing the noise level to be adjusted based on user privacy preferences. The system can adapt the amount of noise added in real-time based on the user's chosen privacy parameter, enabling flexible control over the privacy-accuracy trade-off.
Data Source
AI summary
The present system relates a platform for addressing the optimal privacy-accuracy trade-off in the revelation of a user's valuable information to a third party. Specifically, the present system formalizes the privacy-accuracy trade-off in a precise mathematical framework, wherein mathematical formalization captures user's privacy preference with a single parameter. The system possesses a revelation method of user data that is optimal, in the sense of abiding by user's privacy preference while providing the most accurate description to third party subject to the aforementioned privacy preference constraint.