Taxonomy-Based Data Consent Management for Privacy Control
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Solution Overview
Problem
Current data-sharing practices in social networking applications often violate user privacy as third-party partners modify data beyond the scope of initial consent agreements, leading to legal risks and user alienation.
Innovation Solution
A computer system that receives initial consent for data release to third parties based on a predefined taxonomy, determines additional data classifications, and requests secondary consent if the classification differs, ensuring user privacy rights are respected and authorization is granted only when appropriate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the provider shares data with third-party partners to generate additional revenue, then productivity increases, but user privacy is compromised and users may be alienated
Solution Approach 1:
The patent segments data into different classifications using a taxonomy system, allowing the provider to share only specific types of data with third-party partners while maintaining control over other data types. This enables selective data sharing that generates revenue without compromising all user data, thus balancing productivity improvement with privacy protection.
Solution Approach 2:
The patent implements a feedback mechanism where the system monitors what third-party partners do with shared data and notifies users of any unexpected modifications or new data generation. This allows users to provide feedback and revoke consent if necessary, maintaining trust while enabling data sharing for revenue generation.
2Adaptability or versatility
If the provider uses a broad privacy policy consent agreement to share data, then adaptability increases, but reliability decreases due to conflicts of interest and inadequate user protection
Solution Approach 1:
The patent divides data into classified categories within a taxonomy framework, allowing the privacy policy to be more precise and tailored to specific data types rather than using broad, one-size-fits-all consent agreements. This increases reliability by giving users granular control while maintaining adaptability through the structured classification system.
Solution Approach 2:
The patent makes the privacy consent system dynamic by allowing users to review and update their consent preferences based on how third parties use their data. The system adapts to user feedback and changes in data usage patterns, maintaining reliability through ongoing user control rather than static, broad consent agreements.
3Adaptability or versatility
If third-party partners modify data beyond the scope of initial consent, then adaptability increases, but legal risks increase and user privacy is compromised
Solution Approach 1:
The patent implements a monitoring and notification system that tracks what third-party partners do with shared data. When partners modify data or generate new data types, the system detects these changes and notifies users, who can then provide feedback or revoke consent. This feedback loop prevents unauthorized modifications while allowing legitimate adaptability in data processing.
Solution Approach 2:
The patent establishes predefined data classifications and consent parameters before sharing data with third parties. These preliminary structures define the scope of permitted modifications, preventing third parties from making unauthorized changes beyond the agreed-upon scope, thus reducing legal risks while maintaining necessary adaptability.
Data Source
AI summary
A technique for controlling release of data associated with an account is described. During this data-privacy management technique, a computer system provides at least a subset of data associated with an account to a third party based on a first consent (which may be received from a user of the account). This subset may have a first classification based on a taxonomy. Subsequently, the third party may notify the computer system that additional data has been derived from the data. In response, the computer system may determine a second classification of the additional data based on a taxonomy. If this second classification is other than a subset of the first classification, the computer system may request a second consent (for example, from the user).