Advertisement

Privacy-Preserving Location Publishing under Road-Network Constraints

  • Dan Lin
  • Sashi Gurung
  • Wei Jiang
  • Ali Hurson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5982)

Abstract

We are experiencing the expanding use of location-based services such as AT&T TeleNav GPS Navigator and Intel’s Thing Finder. Existing location-based services have collected a large amount of location data, which have great potential for statistical usage in applications like traffic flow analysis, infrastructure planning and advertisement dissemination. The key challenge is how to wisely use the data without violating each user’s location privacy concerns. In this paper, we first identify a new privacy problem, namely inference route problem, and then present our anonymization algorithms for privacy-preserving trajectory publishing. The experimental results have shown that our approach outperforms the latest related work in terms of both efficiency and effectiveness.

Keywords

Road Segment Edit Distance Average Error Rate Candidate Cluster Anonymization Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abul, O., Atzori, M., Bonchi, F., Giannotti, F.: Hiding sensitive trajectory patterns. In: Proc. of ICDM Workshop, pp. 693–698 (2007)Google Scholar
  2. 2.
    Abul, O., Bonchi, F., Nanni, M.: Never walk alone: Uncertainty for anonymity in moving objects databases. In: Proc. of the International Conference on Data Engineering, pp. 376–385 (2008)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 439–450 (2000)Google Scholar
  4. 4.
    Atzori, M., Bonchi, F., Giannotti, F., Pedreschi, D.: Anonymity preserving pattern discovery. The VLDB Journal 17(4), 703–727 (2008)CrossRefGoogle Scholar
  5. 5.
    Bettini, C., Wang, X.S., Jajodia, S.: Protecting Privacy Against Location-Based Personal Identification. In: Jonker, W., Petković, M. (eds.) SDM 2005. LNCS, vol. 3674, pp. 185–199. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Brinkhoff, T.: A framework for generating network-based moving objects (2004), http://www.fh-oow.de/institute/iapg/personen/brinkhoff/generator
  7. 7.
    Gidofalvi, G., Huang, X., Pedersen, T.B.: Privacy-preserving data mining on moving object trajectories. In: Proc. of the International Conference on Data Engineering, pp. 60–68 (2007)Google Scholar
  8. 8.
    Hoh, B., Gruteser, M.: Protecting location privacy through path confusion. In: Proc. of SecureComm., pp. 194–205 (2005)Google Scholar
  9. 9.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Preserving privacy in gps traces via uncertainty-aware path cloaking. In: Proc. of the ACM conference on Computer and Communications Security, pp. 161–171 (2007)Google Scholar
  10. 10.
    Lin, D., Gurung, S., Jiang, W., Hurson, A.: Privacy-preserving location publishing under road-network constraints. Technical Report, http://web.mst.edu/~lindan/others/trajectory.pdf
  11. 11.
    Mokbel, M.F.: Privacy in location-based services: State-of-the-art and research directions. In: Proc. of the International Conference on Mobile Data Management, p. 228 (2007)Google Scholar
  12. 12.
    Nergiz, M.E., Atzori, M., Saygin, Y., Guc, B.: Towards trajectory anonymization: a generalization-based approach. Transactions on Data Privacy 2(1), 47–75 (2009)Google Scholar
  13. 13.
    Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Mining sequential patterns by pattern-growth: The prefixspan approach. IEEE Transactions on Knowledge & Data Engineering 16(1), 1424–1440 (2004)CrossRefGoogle Scholar
  14. 14.
    Pensa, R.G., Monreale, A., Pinelli, F., Pedreschi, D.: Pattern-preserving k-anonymization of sequences and its application to mobility data mining. In: Proc. of the International Workshop on Privacy in Location-Based Applications (2008)Google Scholar
  15. 15.
    Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems 10(5), 571–588 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  16. 16.
    Tanner, J.C.: In search of lbs accountability. In: Telecom Asia (2008)Google Scholar
  17. 17.
    Terrovitis, M., Mamoulis, N.: Privacy preservation in the publication of trajectories. In: Proc. of the International Conference on Mobile Data Management, pp. 65–72 (2008)Google Scholar
  18. 18.
    Wagner, R.A., Fischer, M.J.: The string-to-string correction problem. Journal of the ACM 21(1), 168–173 (1974)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    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 International Conference on Extending Database Technology, pp. 72–83 (2009)Google Scholar
  20. 20.
    Zaki, M.J.: Spade: An efficient algorithm for mining frequent sequences. Machine Learning 42(1/2), 31–60 (2001)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dan Lin
    • 1
  • Sashi Gurung
    • 1
  • Wei Jiang
    • 1
  • Ali Hurson
    • 1
  1. 1.Missouri University of Science and Technology 

Personalised recommendations