Risk-Aware Individual Trajectory Data Publishing With Differential Privacy

Large-scale spatiotemporal data mining has created valuable insights into managing key areas of society and the economy. It encouraged owners to release/publish trajectory datasets. However, ill-informed publication such datasets may lead serious privacy implications for individuals. Moreover, as a major goal protection, balancing utility remains challenging problem due diversity data. user dimension was not considered traditional frameworks, which limits application at global level opposed level. Many researchers overcome this issue by assuming that in dataset generates only one trajectory. Actually, always multiple repetitive trajectories during observation. Only considering cause insufficient protection alone, user's can be manifested many collectively. In addition, it demonstrates strong correlation when using trajectories. If considered, additional information will lost, decreased. article, we propose novel privacy-preserved publishing method, i.e., IDF-OPT, reduce least-information loss guarantee individual privacy. Comprehensive experiments based on an actual benchmark demonstrate proposed method maintains high practicability mining.