Evaluating the Privacy Implications of Frequent Itemset Disclosure

  • Edoardo Serra
  • Jaideep Vaidya
  • Haritha Akella
  • Ashish Sharma
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 502)


Frequent itemset mining is a fundamental data analytics task. In many cases, due to privacy concerns, only the frequent itemsets are released instead of the underlying data. However, it is not clear how to evaluate the privacy implications of the disclosure of the frequent itemsets. Towards this, in this paper, we define the k-distant-IFM-solutions problem, which aims to find k transaction datasets whose pair distance is maximized. The degree of difference between the reconstructed datasets provides a way to evaluate the privacy risk. Since the problem is NP-hard, we propose a 2-approximate solution as well as faster heuristics, and evaluate them on real data.


Inverse Frequent itemset Mining Column generation 


  1. 1.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar
  2. 2.
    Barnhart, C., Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P., Vance, P.H.: Branch-and-price: column generation for solving huge integer programs. Oper. Res. 46, 316–329 (1996)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Borodin, A., Lee, H.C., Ye, Y.: Max-sum diversification, monotone submodular functions and dynamic updates. In: Proceedings of the 31st Symposium on Principles of Database Systems, PODS 2012, pp. 155–166. ACM, New York (2012). http://doi.acm.org/10.1145/2213556.2213580
  4. 4.
    Calders, T.: Itemset frequency satisfiability: complexity and axiomatization. Theor. Comput. Sci. 394(1–2), 84–111 (2008)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Goethals, B., Zaki, M.J.: Fimi03: workshop on frequent itemset mining implementations. In: Third IEEE International Conference on Data Mining Workshop on Frequent Itemset Mining Implementations, pp. 1–13 (2003)Google Scholar
  6. 6.
    Guzzo, A., Moccia, L., Saccà, D., Serra, E.: Solving inverse frequent itemset mining with infrequency constraints via large-scale linear programs. TKDD 7(4), 18 (2013). http://doi.acm.org/10.1145/2541268.2541271
  7. 7.
    Guzzo, A., Saccà, D., Serra, E.: An effective approach to inverse frequent set mining. In: Ninth IEEE International Conference on Data Mining, ICDM 2009, pp. 806–811, December 2009Google Scholar
  8. 8.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 3 (2007)CrossRefGoogle Scholar
  9. 9.
    Mielikainen, T.: On inverse frequent set mining. In: Society, I.C. (ed.) Proceedings of the 2nd Workshop on Privacy Preserving Data Mining (PPDM), pp. 18–23 (2003)Google Scholar
  10. 10.
    Ramesh, G., Maniatty, W., Zaki, M.: Feasible itemeset distributions in data mining: theory and application. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 682–693 (2002)Google Scholar
  11. 11.
    Samarati, P.: Protecting respondents identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001)CrossRefGoogle Scholar
  12. 12.
    Schrijver, A.: Theory of Linear and Integer Programming. John Wiley & Sons Inc., New York (1986)MATHGoogle Scholar
  13. 13.
    Wang, Y., Wu, X.: Approximate inverse frequent itemset mining: Privacy, complexity, and approximation. In: ICDM, pp. 482–489 (2005)Google Scholar
  14. 14.
    Wu, X., Wu, Y., Wang, Y., Li, Y.: Privacy-aware market basket data set generation: an feasible approach for inverse frequent set mining. In: Proceedings of the 5th SIAM International Conference on Data Mining (2005)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  • Edoardo Serra
    • 1
  • Jaideep Vaidya
    • 2
  • Haritha Akella
    • 1
  • Ashish Sharma
    • 1
  1. 1.CS DepartmentBoise State UniversityBoiseUSA
  2. 2.MSIS DepartmentRutgers UniversityNew BrunswickUSA

Personalised recommendations