# Trajectory Clustering and k-NN for Robust Privacy Preserving Spatiotemporal Databases
## 2 Materials and Methods
### System Model
This shows that the security of mobile users is more vulnerable when they are protected by few nearest neighbors. ### Experiments Conclusions

In the context of this work, we carried out research on privacy preserving based on real spatiotemporal data. This paper proposes a \(k\)-anonymity model based on motion vectors that provides anonymity for spatial queries. Specifically, we investigated the problem of \(k\)-anonymity from dimensionality perspective and how the combination of dimensions affects both methods vulnerability, which is estimated based on Algorithm 5. We observed that the inter-attribute combinations or suppression within a record have a powerful revealing effect in the increase of dimensional case. We proved the effectiveness and efficacy of MCL (Method-with-CLustering, Algorithm 4), under specific dimensions combination, by intensive experiments. Finally, the anonymization using clustering (based on all attributes) and attributes' suppression in \(k\)-anonymity set computation is a solution for privacy preserving. **Author Contributions:** Formal analysis, E.D. ; investigation, E.D., M.T; methodology, S.S.; project administration, S.S.; resources, P.G. ; software, P.G. ; supervision, S.S.; visualization, E.D. ; writing--review and editing, E.D., M.T. **Funding:** This research work was funded by General Secretariat for Research and Technology (HSRT), Hellenic Foundation for Research and Innovation (HFRI), and supported by University of Patras. **Acknowledgments:** The authors gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved both content and presentation, and their college [PERSON], for his helpful and constructive instructions during all steps of research process. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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