Delay-Aware Privacy-Preserving Location-Based Services Under Spatiotemporal Constraints

The ubiquitous use of location-based services (LBS) through smart devices produces massive amounts of location data. An attacker, with an access to such data, can reveal sensitive information about users. In this paper, we study location inference attacks based on the probability distribution of historical location data, travel time information between locations using knowledge of a map, and short and long-term observation of privacy-preserving queries. We show that existing privacy-preserving approaches are vulnerable to such attacks. In this context, we propose a novel location privacy-preserving approach, called KLAP, based on the three fundamental obfuscation requirements: minimumk-locations,l-diversity, and privacyareapreservation. KLAP adopts a personalized privacy preference for sporadic, frequent, and continuous LBS use cases. Specifically, it generates a secure concealing region (CR) to obfuscate the user's location and directs that CR to the service provider. The main contribution of this work is twofold. First, a CR pruning technique is devised to establish a balance between privacy and delay in LBS usage. Second, a new attack model called a long-term obfuscated location tracking attack, and its countermeasure is proposed and evaluated both theoretically and empirically. We assess KLAP with two real-world datasets. Experimental results show that it can achieve better privacy, reduced delay, and lower communication costs than existing state-of-the-art methods.