Adaptive Personalized Privacy in Participatory Sensing

The participatory sensing paradigm, through the growing availability of cheap sensors in mobile devices, enables applications of great social and business interest, e.g. electrosmog exposure measurement, early earthquake detection, etc. However, users’ privacy concerns regarding their activity traces need to be adequately addressed first. Existing static privacy-enabling approaches, which hide or obfuscate data, offer some protection at the expense of data value, but no privacy guarantees, while heterogeneous user privacy requirements cannot be met. In this report, we propose a user-side privacy protection scheme that adaptively adjusts its parameters, in order to meet personalized location-privacy protection requirements against adversaries in a measurable manner. As proved by simulation experiments with artificial and real data traces of electrosmog participatory sensing, our approach not only always satisfies personal location-privacy concerns when feasible, but also maximizes data utility (in terms of error, data availability, area coverage), as compared to static privacy-protection schemes.