Privacy-preserving mobile crowd sensing in ad hoc networks

Abstract

The presence of a rich set of embedded sensors on mobile devices has been propelling various sensing applications regarding individual activities and their surrounding environment, and these persuasive sensing-capable mobile devices are pushing the new paradigm of Mobile Crowd Sensing (MCS) from sketch to reality. MCS aims to outsource sensing data collection to Mobile Device Owner (MDO) and it could revolutionize the conventional ways of sensing data collection and processing. Nonetheless, the widespread deployment of MCS gives rise to the privacy concerns from both the MDOs and the Sensing Service Consumers (SSC), especially in the case where MCS relies on untrustworthy third-party infrastructures. This paper proposes three protocols to address the privacy issues of MCS in ad hoc network without depending on any third-parties. It first presents Privacy-Preserving Summation (PPS) protocol to protect the privacy of the SSCs. Next, it puts forward Privacy-Preserving Difference Rank Computation (PPDRC) protocol to ensure the privacy of the MDOs. Finally, it proposes Approximate K-Nearest Neighbor with Privacy Preservation(AKN2P2) to approximately identify the k-nearest neighbors without privacy leaks of both the MDOs and the SSCs. The performance evaluations demonstrate the computation overhead in different settings.