Enabling Proximity Detection While Preserving Location Privacy

S E P T E M B E R / O C T O B E R 2 0 1 5 InsideGNSS 67 Ubiquitous locat ion-aware mobile devices, mainly GPSenabled smartphones, have led to a boom in location-based services (LBS), which have been revolutionizing businesses and lifestyles. Common uses of LBSs include asset tracking, locationbased advertising, emergency roadside service, turn-by-turn navigation, and real-time traffic & road information sharing. A major category in LBSs is proximity-based services, which allow users to search for friends or other points of interest around them. Examples of proximity-based mobile social networking include Apple’s “Find My Friends,” Facebook’s “Nearby Friends,” Tencent’s “WeChat,” Momo, and Nearby. In Find My Friends, a user can see the locations of his friends and get notified when his friends are nearby. In WeChat, a user can find nearby users by the following two ways: • Shake — A user shakes the phone, and the app will find other WeChat users who are also shaking at the moment locally and around the world. Then the user has an opportunity to message them and make new friends. • Look Around — Look Around is like Shake without the shaking. The app simply finds other WeChat users who have been recently in the user’s vicinity. To use an LBS, a user usually has to send his exact location to the service provider, sporadically or frequently. The contextual information attached to user locations may, however, also reveal the users’ habits, interests, activities, health status, and political and religious affiliations. The high level of intrusion and With the proliferation of GPS-enabled mobile devices, location based services have gained in popularity. In particular, proximitybased services are commonly used to help a user interact with other users nearby. However, these services endanger the privacy of users as the location information is provided to the service provider. This article proposes a novel private proximity detection scheme based on partial GPS measurement information. We develop an efficient algorithm for proximity detection and theoretically analyze the false alarm performance. Empirical results validate this scheme and evaluate its performance. © iS to ck ph ot o. co m /m ax us er GPS Confidential Enabling Proximity Detection While Preserving Location Privacy