Preserving Privacy in WiFi Localization With Plausible Dummy Locations

Benefiting from the development of wireless communication, WiFi 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 WiFi 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 WiFi 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 WiFi localization privacy algorithm, this paper proposes to reinforce dummy techniques with plausible dummy location to resist the attacks. This study presents a novel algorithm, referred to as
Location Preservation Algorithm with Plausible Dummies
(LPPD), which is the first attempt towards WiFi 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 study implements a WiFi localization system that integrates the proposed privacy-preserving algorithm with a WiFi fingerprint based indoor localization algorithm. Experiments results are presented to demonstrate the effectiveness and efficiency of our approach when compared with existing algorithms.