Protecting Against Velocity-Based, Proximity-Based, and External Event Attacks in Location-Centric Social Networks

Abstract

Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications (e.g., Foursquare). Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, and so forth. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR. In this work, we protect against a broad range of attacks that breach location privacy using knowledge about (1) maximum user velocity, (2) external events that may occur outside the process of self-reporting locations (e.g., social network posts tagged by peers), and (3) information about mutual proximity between users. Assume user u who reports two consecutive cloaked regions A and B . We consider two distinct protection scenarios: in the first case, the attacker does not have information about the sensitive locations on the map, and the objective is to ensure that u can reach some point in B from any point in A ; in the second case, the attacker knows the placement of sensitive locations, and the objective is to ensure that u can reach any point in B from any point in A . We propose spatial and temporal cloaking transformations to preserve user privacy, and we show experimentally that privacy can be achieved without significant quality-of-service deterioration.