ML Sensitivity Detection for Leaking Browsing Profiles
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems fail to accurately detect and prevent the unintentional leakage of sensitive user information during browsing activities, leading to the provision of discriminatory or predatory digital content.
Innovation Solution
A sensitivity detection system utilizing machine-learning models to identify when user profiles inadvertently reveal sensitive information and implement mitigating actions to prevent such leaks, including modifying user profiles and digital content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user information is collected and shared to provide digital content, then digital content delivery is improved, but sensitive user information is leaked unintentionally
Solution Approach 1:
The patent introduces an intermediary sensitivity detection system that sits between the user information collection system and the digital content delivery system. This intermediary uses machine learning models to detect sensitive information in user profiles before they are shared, blocking only the sensitive portions while allowing non-sensitive information to flow through for legitimate content delivery purposes.
Solution Approach 2:
The system implements feedback loops where the sensitivity detection model continuously monitors user profiles and provides feedback to the profile generation model. When sensitive information is detected, the system adjusts the profile generation parameters to avoid creating profiles that would leak sensitive information, while maintaining the ability to deliver relevant digital content.
2Measurement precision
If machine learning models are used to detect sensitive information, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the sensitivity detection system into multiple specialized machine learning models, each trained to detect specific types of sensitive information (e.g., health information, financial information, political views). This segmentation allows each model to focus on a specific detection task, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The system performs preliminary training of sensitivity detection models using synthetic data generated before deployment. This preliminary action prepares the models in advance with diverse training examples, enabling them to achieve high detection accuracy when deployed in the actual system without requiring complex real-time adjustments.
3Reliability
If user profiles are modified to prevent sensitivity leakage, then privacy protection is improved, but digital content relevance may be reduced
Solution Approach 1:
The system extracts only the sensitive portions of user profiles and removes or masks them, while preserving the non-sensitive information that is necessary for delivering relevant digital content. This selective extraction approach maintains privacy protection for sensitive areas while keeping the profile useful for content recommendation.
Solution Approach 2:
The patent applies different quality standards to different parts of the user profile. Sensitive portions are heavily protected with high anonymity, while non-sensitive portions maintain their original quality and detail level to ensure digital content relevance. This local differentiation allows simultaneous achievement of privacy protection and content relevance.
Data Source
AI summary
The disclosure relates to a sensitivity detection system that accurately and efficiently determines when information based on a user's browsing activity unintentionally reveals private or other sensitive information about the user. For example, the sensitivity detection system generates and utilizes machine learning models for detecting sensitivity to accurately detect when sensitive user information is being leaked from a collection of user information, such as a user profile. Additionally, upon determining that sensitive user information is being revealed, in many instances, the sensitivity detection system performs mitigation actions to stop and/or reduce sensitive user information from being undesirably revealed.