Privacy-preserving publication of trajectories using microaggregation

Huge amounts of movement data are automatically collected by technologies such as GPS, GSM, RFID, etc. Publishing such data is essential to improve transportation, to understand the dynamics of the economy in a region, etc. However, there are obvious threats to the privacy of individuals if their trajectories are published in a way which allows reidentification of the individual behind a trajectory. We contribute to the literature on privacy-preserving publication of trajectories by presenting: i) a distance measure for trajectories which naturally considers both spatial and temporal aspects of trajectories, is computable in polynomial time, and can cluster trajectories not defined over the same time span (something that previously proposed methods could not do); ii) a method to replace a cluster of trajectories by synthetic data that preserve all the visited locations and the number of original trajectories, among other features; iii) a comparison of our method with (k, Δ)-anonymity [1] using trajectories generated by the Brinkhoff's generator [4] in the city of Oldenburg.