A privacy mechanism for mobile-based urban traffic monitoring

In mobile-based traffic monitoring applications, each user provides real-time updates on their location and speed while driving. This data is collected by a centralized server and aggregated to provide participants with current traffic conditions. Successful participation in traffic monitoring applications utilizing participatory sensing depends on two factors: the information utility of the estimated traffic condition, and the amount of private information (position and speed) each participant reveals to the server. We assume each user prefers to reveal as little private information as possible, but if everyone withholds information, the quality of traffic estimation will deteriorate. In this paper, we model these opposing requirements by considering each user to have a utility function that combines the benefit of high quality traffic estimates and the cost of privacy loss. Using a novel Markovian model, we mathematically derive a policy that takes into account the mean, variance and correlation of traffic on a given stretch of road and yields the optimal granularity of information revelation to maximize user utility. We validate the effectiveness of this policy through real-world empirical traces collected during the Mobile Century experiment in Northern California. The validation shows that the derived policy yields utilities that are very close to what could be obtained by an oracle scheme with full knowledge of the ground truth.