PPRP: Preserving Location Privacy for Range-based Positioning in Mobile Networks

In this paper, we propose a privacy-preserving range-based positioning scheme, named PPRP, which can preserve the location privacy of both user equipment (UE) and anchors (ACs) in mobile networks. Specifically, PPRP is established on a decentralized trust-based framework that divides trust between two location management function (LMF) servers. With such a framework, UE and ACs are allowed to securely upload their range/range-difference measurement data to LMF servers using lightweight additive secret sharing techniques (ASS) instead of cumbersome cryptographic operations. Then, PPRP takes secret-shared measurement data as inputs and decomposes UE's location estimation procedures into secure two-party matrix computation sub-protocols, which are elaborately crafted using somewhat homomorphic encryption and randomization techniques to ensure both efficiency and privacy preservation in positioning. Furthermore, to mitigate the negative effects arising from non-line-of-sight (NLoS) ACs, PPRP achieves privacy-preserving residual-based NLoS analysis. To this end, we additionally propose a series of secure two-party sub-protocols to support various non-linear functions, including comparison, division, square root computation, oblivious shuffle and sorting. These sub-protocols serve as fundamental modules that can be effectively combined to perform sophisticated operations of NLoS analysis in a privacy-preserving manner. A comprehensive simulation-based security analysis demonstrates that PPRP can achieve location privacy preservation. Finally, we develop a proof-of-concept prototype and conduct extensive experiments to show PPRP's high performance in terms of positioning accuracy, computational efficiency, and communication complexity.