Privacy-preserving computation for location-based information survey via mobile cloud computing

Location-Based Service (LBS) has become pervasive, riding along the trend of mobile application proliferation. Given its high utility value, LBS, however, present serious privacy concerns for cautious users. In this paper, we investigate privacy preserving for location-based information survey application, which calculates the geographic distribution of user´s information. The design objective is twofold: i) calculate an information distribution for a pool of mobile users, and ii) protecting the location and value privacy of individual user, in the presence of malicious servers and possible corrupted users. Our proposed solution leverages a mobile cloud computing paradigm, in which each mobile device is replicated with a system-level clone in a proximate cloud environment. The computing of distribution function is distributed among the set of cloud clones, via a peer-to-peer (P2P) protocol. Compared to the approaches based on centralized server or aggregate proxy, our scheme is advantageous in avoiding single point of failure/attack, load balancing and overhead reduction. Simulation results verify these advantages and suggest that our proposed scheme is suitable for large scale applications.