Efficient Enforcement of Privacy for Moving Object Trajectories

  • Anuj Shanker Saxena
  • Vikram Goyal
  • Debajyoti Bera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8303)

Abstract

Information services based on identity and current location is already very popular among Internet and Mobile users, and a recent trend that is gaining acceptance is those based on annotated routes of travel, which we call as trajectories. We are motivated by the need of some users to reveal neither their identity nor location. This is not impossible since exact location can be substituted by an enclosing region, and the identity can be anonymised by relaying all queries through a proxy. However, when users are continuously making queries throughout a session, their queries can contain sufficient correlation which can identify them and/or their queries. Furthermore, a large region will fetch unnecessary search results degrading search quality. This problem of guaranteeing privacy, using smallest possible enclosing regions is NP-hard in general. We propose an efficient greedy algorithm which guarantees a user specified level of location and query privacy, namely k-anonymity and l-diversity, throughout a session and all the while trying to not significantly compromise service quality. Our algorithm, running on the proxy, makes use of trajectories to find similar users whose trajectories are also close by (using appropriate notions of similarity and closeness) for privacy enforcement. We give an indexing structure for efficiently storing and retrieving past trajectories, and present extensive experimental results comparing our approach with other similar approaches.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dewri, R., Ray, I., Ray, I., Whitley, D.: Query m-invariance: Preventing query disclosures in continuous location-based services. In: IEEE MDM (2010)Google Scholar
  2. 2.
    Xu, T., Cai, Y.: Exploring historical location data for anonymity preservation in location-based services. In: INFOCOM 2008: Proceeding of 27th Conference on Computer Communications (2008)Google Scholar
  3. 3.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S.: Path prediction and predictive range querying in road network databases. The VLDB Journal 19(4), 585–602 (2010)CrossRefGoogle Scholar
  4. 4.
    Gruteser, M., Grunwald, D.: Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking. In: MobiSys 2003: Proceedings of the 1st International Conference on Mobile Systems, Applications and Services (2003)Google Scholar
  5. 5.
    Chow, C.-Y., Mokbel, M.F., Liu, X.: A peer-to-peer spatial cloaking algorithm for anonymous location-based service. In: GIS 2006: Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems (2006)Google Scholar
  6. 6.
    Gedik, B., Liu, L.: Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms. IEEE TMC: IEEE Transactions on Mobile Computing 7 (2008)Google Scholar
  7. 7.
    Mokbel, M.F., Chow, C.-Y., Aref, W.G.: The new Casper: query processing for location services without compromising privacy. In: VLDB 2006: Proceedings of the 32nd International Conference on Very Large Data Bases (2006)Google Scholar
  8. 8.
    Bamba, B., Liu, L., Pesti, P., Wang, T.: Supporting anonymous location queries in mobile environments with privacygrid. In: WWW 2008: Proceeding of the 17th International Conference on World Wide Web (2008)Google Scholar
  9. 9.
    Bettini, C., Mascetti, S., Wang, X.S., Jajodia, S.: Anonymity in Location-Based Services: Towards a General Framework. In: MDM 2007: Proceedings of the International Conference on Mobile Data Management (2007)Google Scholar
  10. 10.
    Chow, C.-Y., Mokbel, M.F.: Enabling private continuous queries for revealed user locations. In: Papadias, D., Zhang, D., Kollios, G. (eds.) SSTD 2007. LNCS, vol. 4605, pp. 258–275. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Saxena, A.S., Pundir, M., Goyal, V., Bera, D.: Preserving location privacy for continuous queries on known route. In: Jajodia, S., Mazumdar, C. (eds.) ICISS 2011. LNCS, vol. 7093, pp. 265–279. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Beresford, A.R., Stajano, F.: Location Privacy in Pervasive Computing. IEEE Pervasive Computing 2(1) (2003)Google Scholar
  13. 13.
    Palanisamy, B., Liu, L.: Mobimix: Protecting location privacy with mix-zones over road networks. In: 27th ICDE 2011 (2011)Google Scholar
  14. 14.
    Shang, S., Ding, R., Yuan, B., Xie, K., Zheng, K., Kalnis, P.: User oriented trajectory search for trip recommendation. In: EDBT (2012)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Anuj Shanker Saxena
    • 1
  • Vikram Goyal
    • 2
  • Debajyoti Bera
    • 2
  1. 1.National Defence AcademyKhadakwaslaIndia
  2. 2.Indraprastha Institute of Information Technology DelhiNew DelhiIndia

Personalised recommendations