Preserving Privacy in Wi-Fi Localization with Plausible Dummy Locations

Benefiting from the development of wireless communication, Wi-Fi localization plays a vital role in various mobile applications. However, users’ locations are breached by untrusted localization servers, thus disclosing users’ location privacy and other sensitive personal information. Existing works on Wi-Fi localization preservation employed homomorphic encryption, which incurs extremely high overheads. On the other hand, lightweight methods like k-anonymity and dummy-based approaches are hardly used for privacy preservation in Wi-Fi localization scenarios due to their inherent drawbacks, i.e., k-anonymity approaches rely on trusted third parties, while dummy-based approaches are susceptible to spatio-temporal correlation attacks. To design an effective yet lightweight Wi-Fi localization privacy algorithm, this chapter proposes to reinforce dummy techniques with plausible dummy location to resist the attacks. This chapter presents a novel algorithm, referred to as Location Preservation Algorithm with Plausible Dummies (LPPD), which is the first attempt toward Wi-Fi localization privacy preservation using dummy techniques. The benefits of the algorithm are three-fold: First, it is lightweight. Second, it does not rely on trusted third parties. Third, it can resist spatio-temporal correlation attacks. Moreover, this chapter implements a Wi-Fi localization system that integrates the proposed privacy-preserving algorithm with a Wi-Fi fingerprint-based indoor localization algorithm. Experiments results are presented to demonstrate the effectiveness and efficiency of our approach when compared with existing algorithms.