Privacy and Utility Preserving Trajectory Data Publishing for Intelligent Transportation Systems

Nowadays, the extensive collection and storage of massive personal GPS data in intelligent transportation systems every day provide great convenience for trajectory data analysis and mining research, thus bringing valuable information for real-life applications. Yet, protecting personal privacy is also more challenging in the smart environment. When trajectories of individuals are published together with their sensitive attributes such as disease, income etc., one can use partial trajectory knowledge for identity, sensitive locations, and sensitive values of target individuals. We present $(\alpha, K)_{L}$ -privacy model and an anonymization scheme aimed at ${I}$ dentifying and ${E}$ liminating ${V}$ iolating privacy ${S}$ ubtrajectories (IEVS), to prevent privacy disclosure while preserving the accuracy and high quality of published trajectories. In particular, IEVS employs three anonymization techniques, i.e., trajectory splitting, location suppression, and sensitive value generalization to eliminate all subtrajectories violating $(\alpha, K)_{L}$ -privacy principle. Experiments show our scheme is effective to improve the data utility of anonymized trajectories when compared with previous work.