Stochastic Privacy Service Probability-Based Data Access Control
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Solution Overview
Problem
Users face challenges in controlling the privacy of their personal data shared with online service providers, as existing consent agreements are often broad and lack transparency regarding data utilization probabilities, leading to unclear risks and incentives.
Innovation Solution
A stochastic privacy program is introduced, which provides users with a personal data utilization probability guarantee, allowing them to agree to specific probabilities of data utilization in exchange for incentives, ensuring that their data is used only within agreed-upon limits and incentivizing participation through rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If broad consent agreements are used to obtain user permission for data sharing, then service providers can gather extensive user data for personalization and revenue optimization, but users lose control over their data privacy and face unclear risks
Solution Approach 1:
The patent transforms the binary consent model into a probabilistic parameter system where users specify a maximum probability threshold for data utilization. This allows continuous adjustment of privacy control granularity while enabling service providers to utilize data within agreed-upon probability bounds, resolving the contradiction between data quantity and user control.
Solution Approach 2:
The system dynamically selects users for data collection based on real-time probability thresholds and available data subjects. Instead of static broad consent, the system adaptively determines which users' data will be collected based on current pool composition and utilization probabilities, maintaining user control while enabling flexible data gathering.
2Productivity
If user data is utilized extensively for service optimization and targeted advertising, then service providers can enhance revenues and improve services, but user privacy risks increase with unclear utilization probabilities
Solution Approach 1:
The system implements feedback mechanisms where service providers receive signals about which users are currently selected for data collection based on probability thresholds. This enables optimization of service delivery and advertising targeting while maintaining transparent privacy risk levels, as users know the probability bounds within which their data may be utilized.
Solution Approach 2:
The patent introduces a probabilistic selection mechanism as an intermediary between user consent and actual data utilization. This intermediary layer ensures that even when users have given permission, data is only collected when probabilistic conditions are met, thereby controlling privacy risk while enabling service optimization.
3Adaptability or versatility
If complex Terms of Service are used to obtain user permission, then service providers can legally share user data with third parties, but users face difficulty understanding the actual risks and incentives
Solution Approach 1:
The patent replaces complex legal terminology with simple probabilistic parameters that users can intuitively understand. Instead of navigating complex Terms of Service, users interact with clear probability thresholds that directly represent their privacy risk exposure and data utilization preferences, making risk assessment transparent and accessible.
Data Source
AI summary
A stochastic privacy service provider may provide users with a guaranteed upper bound on a probability that personal data will be accessed while enabling the services to collect data that can be used to enhance its services. Users may receive incentives to become participants in a stochastic privacy program. The stochastic privacy provider may employ one or more probabilistic and decision-theoretic methods to determine which participants' personal data should be sought while guaranteeing that the probability of personal data being accessed is smaller than the mutually agreed upon probability of access. The probability of access may be on a per time basis. The stochastic privacy provider may access coalescenses of the personal data of sets of multiple people, where a maximum probability is given for accessing statistical summaries of personal data computed from groups of people that are of at least some guaranteed size.