Detecting Privacy Violations in Multiple Views Publishing

  • Deming Dou
  • Stéphane Coulondre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7447)

Abstract

We present a sound data-value-dependent method of detecting privacy violations in the context of multiple views publishing. We assume that privacy violation takes the form of linkages, that is, identifier-privacy value pair appearing in the same data record. At first, we perform a theoretical study of the following security problem: given a set of views to be published, if linking of two views does not violate privacy, how about three or more of them? And how many potential leaking channels are there? Then we propose a pre-processing algorithm of views which can turn multi-view violation detection problem into the single view case. Next, we build a benchmark with publicly available data set, Adult Database, at the UC Irvine Machine Learning Repository, and identity data set generated using a coherent database generator called Fake Name Generator on the internet. Finally, we conduct some experiments via Cayuga complex event processing system, the results demonstrate that our approach is practical, and well-suited to efficient privacy-violation detection.

Keywords

Privacy violation Multi-view publishing Pre-processing algorithm Cayuga system 

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References

  1. 1.
    Cao, J., Carminati, B., Ferrari, E., Tan, K.-L.: Castle: A delay-constrained scheme for ks-anonymizing data streams. In: ICDE, pp. 1376–1378 (2008)Google Scholar
  2. 2.
    Demers, A., Gehrke, J., Cayuga, B.P.: A general purpose event monitoring system. In: CIDR, pp. 412–422 (2007)Google Scholar
  3. 3.
    Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: A survey of recent developments. ACM Comput. Surv. 42(4), 1–53 (2010)CrossRefGoogle Scholar
  4. 4.
    Kifer, D., Gehrke, J.: l-diversity: Privacy beyond k-anonymity. In: ICDE 2006: Proceedings of the 22nd International Conference on Data Engineering, p. 24 (2006)Google Scholar
  5. 5.
    Li, J., Ooi, B.C., Wang, W.: Anonymizing streaming data for privacy protection. In: ICDE 2008: Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, pp. 1367–1369. IEEE Computer Society, Washington, DC (2008)CrossRefGoogle Scholar
  6. 6.
    Li, N., Li, T., Venkatasubramanian, S.: t-closeness: Privacy beyond k-anonymity and l-diversity. In: ICDE (2007)Google Scholar
  7. 7.
    Miklau, G., Suciu, D.: A formal analysis of information disclosure in data exchange. In: SIGMOD 2004: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, pp. 575–586. ACM, New York (2004)CrossRefGoogle Scholar
  8. 8.
    Samarati, P., Sweeney, L.: Generalizing data to provide anonymity when disclosing information. Technical report (March 1998)Google Scholar
  9. 9.
    Vincent, M.W., Mohania, M., Iwaihara, M.: Detecting privacy violations in database publishing using disjoint queries. In: EDBT 2009: Proceedings of the 12th International Conference on Extending Database Technology, pp. 252–262. ACM, New York (2009)CrossRefGoogle Scholar
  10. 10.
    Wang, W., Li, J., Ai, C., Li, Y.: Privacy protection on sliding window of data streams. In: COLCOM 2007: Proceedings of the 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing, pp. 213–221. IEEE Computer Society, Washington, DC (2007)CrossRefGoogle Scholar
  11. 11.
    Yao, C., Wang, X.S., Jajodia, S.: Checking for k-anonymity violation by views. In: VLDB 2005: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 910–921. VLDB Endowment (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Deming Dou
    • 1
  • Stéphane Coulondre
    • 1
  1. 1.CNRS, INSA-Lyon, LIRIS, UMR5205Université de LyonLyonFrance

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