Faster privacy‐preserving location proximity schemes for circles and polygons

Abstract

In the last decade, location information became easily obtainable using off-the-shelf mobile devices. This gave momentum to developing location-based services (LBSs) such as location proximity detection, which can be used to find friends or taxis nearby. LBSs can, however, be easily misused to track users, which draws attention to the need for protecting the privacy of these users. In this work, the authors address this issue by designing, implementing, and evaluating multiple algorithms for privacy-preserving location proximity (PPLP) that are based on different secure computation protocols. Their PPLP protocols support both circle and polygon range queries and have runtimes from a few to some hundreds of milliseconds and bandwidth requirements from a few hundreds of bytes to one megabyte. Consequently, they are well suited for different scenarios and offer faster runtimes and savings in bandwidth and computational power as well as security improvements compared to previous PPLP schemes. In addition, the computationally most expensive parts of the PPLP computation can be precomputed in their protocols, such that the input-dependent online phase runs in just a few milliseconds.