A Distributed Privacy-Preserving Mechanism for Mobile Urban Sensing Applications

In urban sensing applications, participants carry mobile devices that collect sensor readings annotated with spatio-temporal information. However, such annotations put the participants' privacy at stake, as they can reveal their whereabouts and habits to the urban sensing campaign administrators. A solution to protect the participants' privacy is to apply the concept of k-anonymity. In this approach, the reported participants' locations are modified such that at least k - 1 other participants appear to share the same location, and hence become indistinguishable from each other. In existing implementations of k-anonymity, the participants need to reveal their precise locations to either a third party or other participants in order to find k - 1 other participants. As a result, the participants' location privacy may still be endangered in case of ill-intentioned third-party administrators and/or participants. We tackle this challenge by proposing a novel approach that supports the participants in their search for other participants without disclosing their exact locations to any other parties. To evaluate our approach, we conduct a threat analysis and study its feasibility by means of extensive simulations using a real-world dataset.