Privacy-Aware Mobile Sensing In Vehicular Networks

Multiple sensors with different functions on sensor-enabled devices on a mobile vehicle are generating large amount of data in real time which consists of a vehicular user's instant context. Context-aware services and applications utilize users' contexts to provide personalized services in return. However, most of the context-aware applications are selfish and very curious of users' sensitive information. In this work, we propose a framework for privacy preserving mobile sensing by caching on mobile vehicles. The framework combines a context releasing component with sensitive data caching capability to achieve strong contextual privacy for mobile vehicles. The main strength of our approach is to improve the privacy level by data caching for sensitive contexts as well as the quality of query results. Finally, we conduct simulations to evaluate our framework for effectiveness and efficiency.