Trajectory anonymization from a time series perspective

In a world of constant technological evolution, the expansion of location tracking technologies is a fact. Most of us may have a device which can tell us our location with more or less accuracy. Most of those devices collect our location information and, usually, the trajectories we make. Publishing this information is essential for companies to improve their marketing strategies, for the traffic department in order to have some control about the traffic, and for many other entities. With the publishment of this information an important problem arises, the privacy. On the literature, many approaches to trajectory protection are provided. We contribute to the scene providing a framework with a protection method and measures for both the computation of the perturbation added to the data, and the risk of linking a protected trajectory to its original.