Energy-Efficient and Privacy-Preserving Range Query in Participatory Sensing

With the advancement of sensor-embedded mobile electronic devices, participatory sensing has attracted more and more attention because it can collect data, analyze information and outsource tasks to those devices' users. However, range query results obtained at the requesters and dissemination data produced at the providers are usually sensitive and private to be disclosed, e.g., users' locations, identities, behaviors, etc. It is an important but challenging problem to keep data privacy from different users while still maintaining low energy consumption. In this paper, we address this issue and propose HEAP, a High Energy-efficient and privAcy-preserving range query framework in Participatory sensing. Our solution consists of a separation of privilege model, a characteristic value construction and a modular verification scheme. Extensive analysis and experimental results show that HEAP achieves the energy efficiency, security and accountability requirements. Furthermore, to our best knowledge this is the first work that a series model selection methods are reachable in participatory sensing scenarios.