Perturbation-driven anonymization of trajectories

Trajectory datasets are becoming more and more popular due to the massive usage of GPS devices. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We provide privacy protection by (1) first enforcing k-anonymity, meaning every released information refers to at least k users/trajectories, (2) then reconstructing randomly a representation of the original dataset from the anonymization. We present a utility metric that maximizes the probability of a good representation and propose trajectory anonymization techniques to address time and space sensitive applications. The experimental results over synthetic trajectory datasets show the effectiveness of the proposed approach.