Entity Context Obfuscation via Sensor Correlation Analysis
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Solution Overview
Problem
Existing recommender systems fail to effectively obfuscate entity context in sensory environments, leading to unauthorized sharing of personal information due to lack of privacy protection in contexts like surveillance and public domains.
Innovation Solution
A system and method that analyze entity data and sensory environment data from multiple sources to determine a correlation score for features, recommending modifications to obfuscate the entity context based on these scores, utilizing a processor-executable instructions framework that includes an input module, entity analyzer, sensory environment analyzer, obfuscation manager, and learning module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If entity context information is shared in public domain through surveillance devices, then monitoring and detection capability is improved, but privacy protection deteriorates leading to unauthorized sharing of personal information
Solution Approach 1:
The system segments entity context information into multiple features (appearance, behavior, location, etc.) and processes each feature separately through correlation analysis with sensory environment data, enabling selective obfuscation of specific features while maintaining detection capability for others
Solution Approach 2:
The system changes the parameter of feature visibility by dynamically adjusting which features are obfuscated based on correlation scores, transforming the state of information disclosure from fixed to adaptive, thereby protecting privacy while maintaining necessary detection functions
2Measurement precision
If multiple sensors are deployed to sense entities in various scenarios, then surveillance coverage and detection accuracy are improved, but the risk of context leakage and privacy exposure increases
Solution Approach 1:
The system introduces an intermediary processing layer that receives data from multiple sensors, analyzes correlations between entity features and sensory environment, and selectively obfuscates high-correlation features before information is stored or shared, preventing context leakage while maintaining surveillance accuracy
Solution Approach 2:
The system implements feedback mechanisms where correlation scores derived from sensory environment data continuously inform which features require obfuscation, creating a closed-loop system that adapts to prevent context leakage based on real-time analysis of surveillance data patterns
Data Source
AI summary
Systems and methods for providing recommendations to obfuscate an entity context in a sensory environment are described. In one implementation, the method comprises receiving entity data and sensory environment data from a plurality of sources. Further, the method comprises analyzing the entity data to obtain categorized entity data. The categorized entity data comprises a plurality of features indicating characteristics of the entity context. Further, the method comprises analyzing the sensory environment data to obtain categorized sensory environment data. Further, the method comprises determining a correlation score for each of the plurality of features by correlating the categorized entity data and the categorized sensory environment data. Further, the method comprises recommending at least one of the plurality of features, based on the correlation score, to obfuscate the entity context in the sensory environment.