Advertisement

k-Anonymous Patterns

  • Maurizio Atzori
  • Francesco Bonchi
  • Fosca Giannotti
  • Dino Pedreschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3721)

Abstract

It is generally believed that data mining results do not violate the anonymity of the individuals recorded in the source database. In fact, data mining models and patterns, in order to ensure a required statistical significance, represent a large number of individuals and thus conceal individual identities: this is the case of the minimum support threshold in association rule mining. In this paper we show that this belief is ill-founded. By shifting the concept of k-anonymity from data to patterns, we formally characterize the notion of a threat to anonymity in the context of pattern discovery, and provide a methodology to efficiently and effectively identify all possible such threats that might arise from the disclosure of a set of extracted patterns.

Keywords

Association Rule Frequent Itemsets Association Rule Mining Support Threshold Minimum Support Threshold 
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.

References

  1. 1.
    Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the twentieth ACM PODS (2001)Google Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD (1993)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceedings of the 2000 ACM SIGMOD on Management of Data (2000)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: Proceedings of the Twentieth VLDB (1994)Google Scholar
  5. 5.
    Atzori, M., Bonchi, F., Giannotti, F., Pedreschi, D.: k-anonymous patterns. Technical Report 2005-TR-17, ISTI - C.N.R (2005)Google Scholar
  6. 6.
    Calders, T., Goethals, B.: Mining all non-derivable frequent itemsets. In: Proceedings of the 6th PKDD (2002)Google Scholar
  7. 7.
    Fule, P., Roddick, J.F.: Detecting privacy and ethical sensitivity in data mining results. In: Proc. of the 27th conference on Australasian computer science (2004)Google Scholar
  8. 8.
    Hand, D., Mannila, H., Smyh, P.: Principles of Data Mining. MIT Press, Cambridge (2001)Google Scholar
  9. 9.
    Kantarcioglu, M., Jin, J., Clifton, C.: When do data mining results violate privacy? In: Proceedings of the tenth ACM SIGKDD (2004)Google Scholar
  10. 10.
    Knuth, D.: Fundamental Algorithms. Addison-Wesley, Reading (1997)MATHGoogle Scholar
  11. 11.
    Oliveira, S.R.M., Zaiane, O.R., Saygin, Y.: Secure association rule sharing. In: Proc.of the 8th PAKDD (2004)Google Scholar
  12. 12.
    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  13. 13.
    Pei, J., Han, J., Wang, J.: Closet+: Searching for the best strategies for mining frequent closed itemsets. In: SIGKDD 2003 (2003)Google Scholar
  14. 14.
    Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty Fuzziness and Knowledge-based Systems 10(5) (2002)Google Scholar
  15. 15.
    Sweeney, L.: k-anonymity privacy protection using generalization and suppression. International Journal on Uncertainty Fuzziness and Knowledge-based Systems 10(5) (2002)Google Scholar
  16. 16.
    Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. SIGMOD Rec. 33(1), 50–57 (2004)CrossRefGoogle Scholar
  17. 17.
    Zaki, M.J., Hsiao, C.-J.: Charm: An efficient algorithm for closed itemsets mining. In: 2nd SIAM International Conference on Data Mining (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Maurizio Atzori
    • 1
    • 2
  • Francesco Bonchi
    • 2
  • Fosca Giannotti
    • 2
  • Dino Pedreschi
    • 1
  1. 1.Pisa KDD Laboratory, Computer Science DepartmentUniversity of PisaItaly
  2. 2.Pisa KDD Laboratory, ISTI – CNRPisaItaly

Personalised recommendations