A Perturbed Compressed Sensing Protocol for Crowd Sensing

Crowd sensing network is a data-centric network consisting of many participants uploading environmental data by smart mobile devices or predeployed sensors; however, concerns about communication complexity and data confidentiality arise in real application. Recently, Compressed Sensing (CS) is a booming theory which employs nonadaptive linear projections to reduce data quantity and then reconstructs the original signal. Unfortunately, privacy issues induced by untrusted network still remain to be unsettled practically. In this paper, we consider crowd sensing using CS in wireless sensor network (WSN) as the application scenario and propose a data collection protocol called perturbed compressed sensing protocol (PCSP) to preserve data confidentiality as well as its practicality. At first, we briefly introduce the CS theory and three factors correlated with reconstruction effect. Secondly, a secure CS-based framework using a secret disturbance is developed to protect raw data in WSN, in which each node collects, encrypts, measures, and transmits the sampled data in our protocol. Formally, we prove that our protocol is CPA-secure on the basis of a theorem. Finally, evaluation on real and simulative datasets shows that our protocol could not only achieve higher efficiency than related algorithms but also protect signal’s confidentiality.