Exploiting Service Similarity for Privacy in Location-Based Search Queries

Location-based applications utilize the positioning capabilities of a mobile device to determine the current location of a user, and customize query results to include neighboring points of interests. However, location knowledge is often perceived as personal information. One of the immediate issues hindering the wide acceptance of location-based applications is the lack of appropriate methodologies that offer fine grain privacy controls to a user without vastly affecting the usability of the service. While a number of privacy-preserving models and algorithms have taken shape in the past few years, there is an almost universal need to specify one's privacy requirement without understanding its implications on the service quality. In this paper, we propose a user-centric location-based service architecture where a user can observe the impact of location inaccuracy on the service accuracy before deciding the geo-coordinates to use in a query. We construct a local search application based on this architecture and demonstrate how meaningful information can be exchanged between the user and the service provider to allow the inference of contours depicting the change in query results across a geographic area. Results indicate the possibility of large default privacy regions (areas of no change in result set) in such applications.