Context-Aware Privacy Model for Proactive Device Setting Control
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Solution Overview
Problem
Existing client devices lack the ability to efficiently and proactively adjust privacy settings based on user context data, leading to potential violations of user privacy preferences.
Innovation Solution
A personalized privacy model is executed on client devices to predict privacy settings and perform proactive privacy execution actions using context data, including adjusting device and application settings, providing notifications, and blocking data access based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual privacy setting adjustments are used, then user control over privacy is maintained, but user time and effort are consumed continuously
Solution Approach 1:
The privacy management system performs self-service by automatically monitoring context data, determining privacy settings, and executing privacy actions without requiring continuous user intervention. The system serves itself by using its own resources (processors, memory, sensors) to manage privacy autonomously based on predetermined rules and machine learning models.
Solution Approach 2:
The system performs preliminary action by establishing predetermined rules and machine learning models in advance that automatically determine privacy settings based on context data. These pre-configured mechanisms enable the system to proactively adjust privacy settings before privacy violations can occur, eliminating the need for reactive manual adjustments.
2Productivity
If automated privacy management is implemented, then user time is saved, but device computing resources are consumed
Solution Approach 1:
The system applies partial action by selectively monitoring only relevant context data and applying privacy rules only when specific conditions are met, rather than continuously processing all possible data. This reduces computing resource consumption while maintaining effective privacy management for critical scenarios.
Solution Approach 2:
The patent replaces complex mechanical user interactions with automated electronic processing through machine learning models and rule-based systems. This substitution enables efficient automated privacy management that requires minimal user effort while optimizing device resource usage through intelligent algorithms.
3Adaptability or versatility
If context data is continuously monitored, then privacy settings can be dynamically adjusted, but bandwidth and processing resources increase
Solution Approach 1:
The system implements periodic action by monitoring context data at specific intervals and triggering privacy setting adjustments only when relevant changes are detected, rather than continuously monitoring all data streams. This periodic approach maintains adaptability while significantly reducing bandwidth and processing resource consumption.
Solution Approach 2:
The system applies local quality by selectively monitoring and processing only specific types of context data that are relevant to privacy settings, rather than uniformly processing all available data. This targeted approach enables dynamic adaptability while minimizing resource usage by focusing computational efforts on critical privacy-related parameters.
Data Source
AI summary
Example embodiments of the present disclosure provide for an example method including obtaining context data associated with a client computing device as input into a personalized privacy model. The example method includes determining, by the personalized privacy model, privacy settings as output. The example method includes performing, by the client computing device, a proactive privacy execution action based on the privacy settings.