Efficient Location Privacy-Aware Forwarding in Opportunistic Mobile Networks

Abstract

This paper proposes a novel fully distributed and collaborative k-anonymity protocol (LPAF) to protect users' location information and ensure better privacy while forwarding queries/replies to/from untrusted location-based service (LBS) over opportunistic mobile networks (OppMNets). We utilize a lightweight multihop Markov-based stochastic model for location prediction to guide queries toward the LBS's location and to reduce required resources in terms of retransmission overheads. We develop a formal analytical model and present theoretical analysis and simulation of the proposed protocol performance. We further validate our results by performing extensive simulation experiments over a pseudorealistic city map using map-based mobility models and using real-world data trace to compare LPAF to existing location privacy and benchmark protocols. We show that LPAF manages to keep higher privacy levels in terms of k-anonymity and quality of service in terms of success ratio and delay, as compared with other protocols, while maintaining lower overheads. Simulation results show that LPAF achieves up to an 11% improvement in success ratio for pseudorealistic scenarios, whereas real-world data trace experiments show up to a 24% improvement with a slight increase in the average delay.