We present our ongoing work on user data and contextual privacy preservation in mobile devices through semantic reasoning. Recent advances in context modeling, tracking and collaborative localization have led to the emergence of a new class of smart phone applications that can access and share embedded sensor data. Unfortunately, this also means significant amount of user context information is now accessible to applications and potentially others, creating serious privacy and security concerns. Mobile OS frameworks like Android lack mechanisms for dynamic privacy control. We show how data flow among applications can be successfully filtered at a much more granular level using semantic web driven technologies that model device location, surroundings, application roles as well as context-dependent information sharing policies.
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