Private Aggregation of Trajectories

In this paper we consider the problem of aggregating multiple user-generated tracks in a differentially private manner. For this problem we propose a new aggregation algorithm that adds noise sufficient enough to guarantee privacy while preserving the utility of the aggregate. Under natural and simple assumptions, we also show that this algorithm has provably good guarantees.