On Designing Resilient Location-Privacy Obfuscators

The success of location-based services is growing together with the diffusion of GPS-equipped smart devices. As a consequence, privacy concerns are raising year by year. Location privacy is becoming a major interest in research and industry world, and many solutions have been proposed for it. One of the simplest and most flexible approaches is obfuscation, in which the precision of location data is artificially degraded before disclosing it. In this paper, we present an obfuscation approach capable of dealing with measurement imprecision, multiple levels of privacy, untrusted servers and adversarial knowledge of the map. We estimate its resistance against statistical-based deobfuscation attacks, and we improve it by means of three techniques, namely extreme vectors, enlarge-and-scale and hybrid vectors.