AI Data-Sharing Control for User Privacy and Personalization
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Solution Overview
Problem
Users face challenges in navigating complex data collection and sharing practices across digital platforms, leading to unintended data sharing and a lack of control over their personal information, despite privacy regulations like GDPR and CCPA.
Innovation Solution
An AI module installed on user devices monitors interactions, determines user preferences, and predicts data to share with remote computing resources, allowing users to manage and monetize their data while ensuring compliance with privacy regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users accept default settings or provide blanket consent to simplify data management, then ease of operation is improved, but user privacy control and data protection deteriorate
Solution Approach 1:
The AI module automatically manages data sharing decisions by monitoring user interactions, determining preferences, and predicting appropriate data to share without requiring manual user input. The system serves itself by making intelligent decisions about data disclosure based on learned user behavior patterns and privacy preferences.
Solution Approach 2:
The system continuously monitors user interactions with remote computing resources and uses this feedback to refine the machine learning model's predictions. This feedback loop enables the AI module to adapt to changing user preferences and improve its data sharing decisions over time, balancing ease of operation with privacy protection.
2Reliability
If users manually review and manage cookie policies and data sharing settings, then user privacy control is improved, but ease of operation deteriorates due to complexity
Solution Approach 1:
The AI module acts as an intermediary between the user and the complex data sharing systems. It translates user interactions into meaningful preference information and automatically generates appropriate data sharing responses, shielding users from the complexity of underlying cookie policies and data management systems.
Solution Approach 2:
The patent replaces manual mechanical processes of reviewing and managing data sharing settings with an automated AI-based system. The machine learning model substitutes for human decision-making in data sharing, using patterns from user interactions to automatically determine appropriate data to share while maintaining privacy control.
3Adaptability or versatility
If comprehensive data collection and sharing is implemented to enable personalized experiences, then adaptability is improved, but user privacy control deteriorates
Solution Approach 1:
The AI module applies partial action by selectively sharing only the specific data elements that are both useful for personalization and aligned with user preferences. Rather than comprehensive data sharing, the system determines and shares only the necessary minimum data required for effective personalization, reducing privacy intrusion while maintaining adaptability.
Solution Approach 2:
The system applies local quality by tailoring data sharing decisions to specific contexts and user preferences rather than applying uniform data collection policies. The AI module analyzes individual user interactions and determines which data elements are most relevant for each user's personalization needs, creating localized data sharing strategies that balance personalization with privacy.
Data Source
AI summary
Methods and systems for sharing user information with computing resources over a computer network. The method includes monitoring user interactions of a user device with at least one remote computing resource using a monitoring module installed on the user device. User preference information is determined based on the user interactions and stored in a preference database. A machine learning prediction module is trained based on the user preference information. In response to establishing a connection of the user device with at least one specific remote computing resource, the machine learning prediction module predicts information to share with the at least one specific remote computing resource based on the user preference information. The predicted information is then transmitted to the at least one specific computing resource.