Opportunistic privacy preserving monitoring∗

In this paper we present an approach to support privacy in opportunistic sensing. In particular, we use compact and privacy preserving representations of data (or sketches), that allow us to compute relevant statistics over data without disclosing users’ sensitive information (e.g. locations). We exemplify our approach by referring to the important application of noise pollution monitoring. We present preliminary experimental results showing that sketches can actually be employed to produce accurate environmental maps, at the same time preserving users’ privacy.