A Clustering-Based Approach for Personalized Privacy Preserving Publication of Moving Object Trajectory Data

  • Samaneh Mahdavifar
  • Mahdi Abadi
  • Mohsen Kahani
  • Hassan Mahdikhani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7645)


With the growing prevalence of location-aware devices, the amount of trajectories generated by moving objects has been dramatically increased, resulting in various novel data mining applications. Since trajectories may contain sensitive information about their moving objects, so they ought to be anonymized before making them accessible to the public. Many existing approaches for trajectory anonymization consider the same privacy level for all moving objects, whereas different moving objects may have different privacy requirements. In this paper, we propose a novel greedy clustering-based approach for anonymizing trajectory data in which the privacy requirements of moving objects are not necessarily the same. We first assign a privacy level to each trajectory based on the privacy requirement of its moving object. We then partition trajectories into a set of fixed-radius clusters based on the EDR distance. Each cluster is created such that its size is proportional to the maximum privacy level of trajectories within it. We finally anonymize trajectories of each cluster using a novel matching point algorithm. The experimental results show that our approach can achieve a satisfactory trade-off between space distortion and re-identification probability of trajectory data, which is proportional to the privacy requirement of each moving object.


privacy preservation trajectory data moving object greedy clustering space distortion re-identification probability 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Frentzos, E., Gratsias, K., Pelekis, N., Theodoridis, Y.: Nearest Neighbor Search on Moving Object Trajectories. In: Medeiros, C.B., Egenhofer, M., Bertino, E. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 328–345. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Lee, J.-G., Han, J., Li, X., Gonzalez, H.: raClass: Trajectory Classification Using Hierarchical Region-based and Trajectory-based Clustering. In: Proc. of the 34th Int. Conf. on Very Large Databases (VLDB 2008), Auckland, New Zealand (2008)Google Scholar
  3. 3.
    Lee, J.-G., Han, J., Whang, K.-Y.: Trajectory Clustering: a Partition-and-Group Framework. In: Proc. of the ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2007), Beijing, China, pp. 593–604 (2007)Google Scholar
  4. 4.
    Li, X., Han, J., Kim, S., Gonzalez, H.: Anomaly Detection in Moving Object. In: Chen, H., Yang, C.C. (eds.) Intelligence and Security Informatics. SCI, vol. 135, pp. 357–381. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Pelekis, N., Kopanakis, I., Kotsifakos, E.E., Frentzos, E., Theodoridis, Y.: Clustering Trajectories of Moving Objects in an Uncertain World. In: Proc. of the 9th IEEE Int. Conf. on Data Mining (ICDM 2009), Miami, USA, pp. 417–427 (2009)Google Scholar
  6. 6.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data 1(1) (2007)Google Scholar
  7. 7.
    Samarati, P.: Protecting Respondents Privacy in Microdata Release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)CrossRefGoogle Scholar
  8. 8.
    Sweeney, L.: k-Anonymity: a Model for Protecting Privacy. International Journal of Uncertainty, Fuzziness Knowledge-Based Systems 10(5), 557–570 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Chen, L., Özsu, M.T., Oria, V.: Robust and Fast Similarity Search for Moving Object Trajectories. In: Proc. of the 24th ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 2005), Maryland, USA, pp. 491–502 (2005)Google Scholar
  10. 10.
    Terrovitis, M., Mamoulis, N.: Privacy Preservation in the Publication of Trajectories. In: Proc. of the 9th Int. Conf. on Mobile Data Management (MDM 2008), Beijing, China, pp. 65–72 (2008)Google Scholar
  11. 11.
    Yarovoy, R., Bonchi, F., Lakshmanan, L.V.S., Wang, W.H.: Anonymizing Moving Objects: How to Hide a MOB in a Crowd? In: Proc. of the 12th Int. Conf. on Extending Database Technology (EDBT 2009), Saint Petersburg, Russia, pp. 72–83 (2009)Google Scholar
  12. 12.
    Nergiz, E., Atzori, M., Saygin, Y.: Towards Trajectory Anonymization: a Generalization-Based Approach. Transactions on Data Privacy 2(1), 47–75 (2009)MathSciNetGoogle Scholar
  13. 13.
    Abul, O., Bonchi, F., Nanni, M.: Anonymization of Moving Objects Databases by Clustering and Perturbation. Information Systems 35(8), 884–910 (2010)CrossRefGoogle Scholar
  14. 14.
    Abul, O., Bonchi, F., Nanni, M.: Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases. In: Proc. of the 24th IEEE Int. Conf. on Data Engineering (ICDE 2008), Cancun, Mexico, pp. 376–385 (2008)Google Scholar
  15. 15.
    Monreale, A., Andrienko, G., Andrienko, N., Giannotti, F., Pedreschi, D., Rinzivillo, S., Wrobel, S.: Movement Data Anonymity through Generalization. Transactions on Data Privacy 3(2), 91–121 (2010)MathSciNetGoogle Scholar
  16. 16.
    Bonchi, F.: Privacy Preserving Publication of Moving Object Data. In: Bettini, C., Jajodia, S., Samarati, P., Wang, X.S. (eds.) Privacy in Location-Based Applications. LNCS, vol. 5599, pp. 190–215. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  17. 17.
    Brinkhoff, T.: Generating Traffic Data. IEEE Data Engineering Bulletin 26(2), 19–25 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Samaneh Mahdavifar
    • 1
  • Mahdi Abadi
    • 2
  • Mohsen Kahani
    • 1
  • Hassan Mahdikhani
    • 3
  1. 1.Department of Computer EngineeringFerdowsi University of MashhadIran
  2. 2.Department of Computer EngineeringTarbiat Modares UniversityTehranIran
  3. 3.School of Computer EngineeringUniversity of Science & TechnologyTehranIran

Personalised recommendations