Hiding Sensitive Itemsets with Minimal Side Effects in Privacy Preserving Data Mining

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 297)

Abstract

Privacy-preserving data mining (PPDM) has become an important issue to hide the confidential or private data before it is shared or published in recent years. In this paper, a novel algorithm is proposed to hide sensitive itemsets through item deletion. Three side effects of hiding failures, missing itemsets, and artificial itemsets are considered to evaluate whether the transactions or the itemsets are required to be deleted for hiding sensitive itemsets. Experiments are then conducted to show the performance of the proposed algorithm in execution time, number of deleted transactions, and number of side effects.

Keywords

Privacy-preserving data mining side effects information hiding data sanitization sensitive itemsets 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Database mining: A performance perspective. IEEE Transactions on Knowledge and Data Engineering 5, 914–925 (1993)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)Google Scholar
  3. 3.
    Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., Verykios, V.: Disclosure limitation of sensitive rules. In: The Workshop on Knowledge and Data Engineering Exchange, pp. 45–52 (1999)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Privacy-Preserving Data Mining. In: ACM SIGMOD International Conference on Management of Data, pp. 439–450 (2000)Google Scholar
  5. 5.
    Amiri, A.: Dare to share: Protecting sensitive knowledge with data sanitization. Decision Support Systems 43, 181–191 (2007)CrossRefGoogle Scholar
  6. 6.
    Berkhin, P.: A survey of clustering data mining techniques. Grouping Multidimensional Data, 25–71 (2006)Google Scholar
  7. 7.
    Dasseni, E., Verykios, V.S., Elmagarmid, A.K., Bertino, E.: Hiding association rules by using confidence and support. In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, p. 369. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Gkoulalas-Divanis, A., Verykios, V.S.: An integer programming approach for frequent itemset hiding. In: ACM International Conference on Information and Knowledge Management, pp. 748–757 (2006)Google Scholar
  9. 9.
    Duraiswamy, K., Manjula, D., Maheswari, N.: Advanced approach in sensitive rule hiding. CCSE Modern Applied Science 3, 98–107 (2009)Google Scholar
  10. 10.
    Gkoulalas-Divanis, A., Verykios, V.S.: Exact knowledge hiding through database extension. IEEE Transactions on Knowledge and Data Engineering 21, 699–713 (2009)CrossRefGoogle Scholar
  11. 11.
    Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering 11, 798–805 (1999)CrossRefGoogle Scholar
  12. 12.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Hong, T.P., Lin, C.W., Wu, Y.L.: Incrementally fast updated frequent pattern trees. Expert Systems with Applications 34, 2424–2435 (2008)CrossRefGoogle Scholar
  14. 14.
    Hong, T.P., Lin, C.W., Yang, K.T., Wang, S.L.: A lattice-based data sanitization approach. IEEE International Conference on Systems, Man, and Cybernetics, 2325–2329 (2011)Google Scholar
  15. 15.
    Hong, T.P., Lin, C.W., Yang, K.T., Wang, S.L.: Using TF-IDF to hide sensitive itemsets. Applied Intelligence 38, 502–510 (2013)CrossRefGoogle Scholar
  16. 16.
    Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. In: The Conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, pp. 3–24 (2007)Google Scholar
  17. 17.
    Leary, D.E.O.: Knowledge discovery as a threat to database security. Knowledge Discovery in Databases, pp. 507–516 (1991)Google Scholar
  18. 18.
    Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Systems with Applications 38, 7419–7424 (2011)CrossRefGoogle Scholar
  19. 19.
    Lin, C.W., Hong, T.P., Chang, C.C., Wang, S.L.: A greedy-based approach for hiding sensitive itemsets by transaction insertion. Journal of Information Hiding and Multimedia Signal Processing 4, 201–227 (2013)Google Scholar
  20. 20.
    Lin, C.W., Hong, T.P.: A survey of fuzzy web mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 3, 190–199 (2013)Google Scholar
  21. 21.
    Modi, C.N., Rao, U.P., Patel, D.R.: Maintaining privacy and data quality in privacy preserving association rule mining. In: International Conference on Computing Communication and Networking Technologies, pp. 1–6 (2010)Google Scholar
  22. 22.
    Wu, Y.H., Chiang, C.M., Chen, A.L.P.: Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data Engineering 19, 29–42 (2007)CrossRefGoogle Scholar
  23. 23.
    Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 401–406 (2001)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chun-Wei Lin
    • 1
    • 2
  • Tzung-Pei Hong
    • 3
    • 4
  • Hung-Chuan Hsu
    • 3
  1. 1.Innovative Information Industry Research Center (IIIRC)ShenzhenP.R. China
  2. 2.Shenzhen Key Laboratory of Internet Information Collaboration School of Computer Science and TechnologyHarbin Institute of Technology Shenzhen Graduate SchoolShenzhenP.R. China
  3. 3.Department of Computer Science and Information EngineeringNational University of KaohsiungKaohsiungTaiwan, R.O.C.
  4. 4.Department of Computer Science and EngineeringNational Sun Yat-sen UniversityKaohsiungTaiwan, R.O.C.

Personalised recommendations