Privacy: preserving trajectory collection

ABSTRACT

In order to provide context--aware Location--Based Services, real location data of mobile users must be collected and analyzed by spatio--temporal data mining methods. However, the data mining methods need precise location data, while the mobile users want to protect their location privacy. To remedy this situation, this paper first formally defines novel location privacy requirements. Then, it briefly presents a system for privacy--preserving trajectory collection that meets these requirements. The system is composed of an untrusted server and clients communicating in a P2P network. Location data is anonymized in the system using data cloaking and data swapping techniques. Finally, the paper empirically demonstrates that the proposed system is effective and feasible.

REFERENCES

• 1: C. Bettini, X. S. Wang, and S. Jajodia. Protecting Privacy Against Location--Based Personal Identification. In Proc. of the VLDB Workshop on Secure Data Management, SDM, 2005.
• 2: Chi-Yin Chow , Mohamed F. Mokbel , Xuan Liu, A peer-to-peer spatial cloaking algorithm for anonymous location-based service, Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, November 10-11, 2006, Arlington, Virginia, USA [doi>10.1145/1183471.1183500]
• 3: Marco Gruteser , Dirk Grunwald, Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking, Proceedings of the 1st international conference on Mobile systems, applications and services, p.31-42, May 05-08, 2003, San Francisco, California [doi>10.1145/1066116.1189037]
• 4: Bugra Gedik , Ling Liu, Location Privacy in Mobile Systems: A Personalized Anonymization Model, Proceedings of the 25th IEEE International Conference on Distributed Computing Systems, p.620-629, June 06-10, 2005 [doi>10.1109/ICDCS.2005.48]
• 5: G. Gidófalvi and T. B. Pedersen. Mining Long, Sharable Patterns in Trajectories of Moving Objects. In Proc. of STDBM, 2006.
• 6: G. Gidófalvi, X. Huang, and T. B. Pedersen Privacy--Preserving Data Mining on Moving Object Trajectories. In Proc. of MDM, 2007.
• 7: Christian S. Jensen , Dan Lin , Beng Chin Ooi , Rui Zhang, Effective Density Queries on ContinuouslyMoving Objects, Proceedings of the 22nd International Conference on Data Engineering, p.71, April 03-07, 2006 [doi>10.1109/ICDE.2006.179]
• 8: Mohamed F. Mokbel , Chi-Yin Chow , Walid G. Aref, The new Casper: query processing for location services without compromising privacy, Proceedings of the 32nd international conference on Very large data bases, September 12-15, 2006, Seoul, Korea
• 9: Ashwin Machanavajjhala , Johannes Gehrke , Daniel Kifer , Muthuramakrishnan Venkitasubramaniam, \ell -Diversity: Privacy Beyond \kappa -Anonymity, Proceedings of the 22nd International Conference on Data Engineering, p.24, April 03-07, 2006 [doi>10.1109/ICDE.2006.1]
• 10: Latanya Sweeney, k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, v.10 n.5, p.557-570, October 2002 [doi>10.1142/S0218488502001648]
• 11: Vassilios S. Verykios , Elisa Bertino , Igor Nai Fovino , Loredana Parasiliti Provenza , Yucel Saygin , Yannis Theodoridis, State-of-the-art in privacy preserving data mining, ACM SIGMOD Record, v.33 n.1, March 2004 [doi>10.1145/974121.974131]
• 12: T.-H. You, W.-C. Peng, and W.-C. Lee. Protecting Moving Trajectories with Dummies. In Proc. of PALMS, 2007.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Spatial databases and GIS

General Terms:
Algorithms

Keywords:
LBS, P2P, anonymity, data cloaking, data mining, data swapping, diversity, moving object trajectories, privacy