Adaptive Secrecy Settings for User Activity-Based Privacy Protection
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Solution Overview
Problem
Existing mechanisms for preventing data leakage during user browsing and searching are primarily manual and ineffective, leading to issues such as spam advertisements, exposure of search history to other users, and visibility of content to unauthorized applications.
Innovation Solution
A system and method utilizing a neural network model to monitor user activity, determine secretive behavior, extract contextual information, and apply predefined secrecy settings based on user activity data and historical behavior to automatically switch to incognito mode.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual settings are used to prevent data leakage, then user privacy protection is improved, but ease of operation deteriorates as users must manually configure and remember privacy settings
Solution Approach 1:
The system automatically monitors user activity patterns and applies secrecy settings without requiring manual user intervention. The device serves itself by detecting secretive behavior patterns and autonomously configuring privacy modes, eliminating the need for users to manually remember and apply privacy settings.
Solution Approach 2:
The system continuously monitors user activity data and provides feedback to the neural network model, which adjusts secrecy settings dynamically based on detected behavior patterns. This closed-loop feedback mechanism enables automatic adaptation to user privacy needs without manual reconfiguration.
2Reliability
If incognito mode is always enabled to prevent data leakage, then user privacy is improved, but productivity deteriorates due to reduced functionality and user experience
Solution Approach 1:
The system dynamically switches between normal browsing mode and secrecy mode based on real-time detection of secretive behavior patterns. Instead of always enabling incognito mode, the system adaptively adjusts privacy settings only when secretive behavior is detected, maintaining full functionality during normal usage while protecting privacy when needed.
Solution Approach 2:
The system changes privacy parameters (such as enabling/disabling history recording, cookie blocking, and search term masking) based on the detected behavior type. This selective parameter adjustment provides privacy protection only for secretive activities while maintaining normal browsing efficiency for routine tasks.
3Measurement precision
If comprehensive monitoring of user activity is implemented to detect secretive behavior, then accuracy of privacy protection is improved, but device complexity increases
Solution Approach 1:
The system replaces complex manual monitoring and analysis mechanisms with a trained neural network model that automatically processes user activity data. The AI model substitutes for what would otherwise require complex rule-based systems or manual analysis, achieving high detection accuracy while managing system complexity through automated machine learning inference.
Data Source
AI summary
A method of applying secrecy settings on a user device is provided. The method includes monitoring, by the user device, user activity data based on usage of one or more applications of the user device, determining, by the user device, whether a user is exhibiting a secretive behavior based on the user activity data and historical behavior of the user, extracting, by the user device, contextual information from other applications, determining, by the user device, whether the contextual information is related to the user activity data, processing, by the user device based on the secretive behavior, the contextual information to provide at least one data stream and at least one attribute associated with the contextual information, determining, by the user device, a predefined secrecy type based on an analysis of the at least one data stream and the at least one attribute, and applying, by the user device, secrecy settings on the user device based on the predefined secrecy type.