Integrated Approach for Privacy Preserving Itemset Mining

  • Barış YıldızEmail author
  • Belgin Ergenç
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 110)


In this work, we propose an integrated itemset hiding algorithm that eliminates the need of pre-mining and post-mining and uses a simple heuristic in selecting the itemset and the item in itemset for distortion. Base algorithm (matrix-apriori) works without candidate generation so efficiency is increased. Performance evaluation demonstrates (1) the side effect (lost itemsets) and time while increasing the number of sensitive itemsets and support of itemset and (2) speed up by integrating the post mining.


Matrix-apriori Privacy preserving data mining Sensitive itemset hiding 


  1. 1.
    Dunham M (2002) Data mining: introductory and advanced topics. Prentice Hall PTR Upper Saddle River, NJ, USAGoogle Scholar
  2. 2.
    Zhang N, Zhao W (2007) Privacy-preserving data mining systems. Computer 40(2):52–58CrossRefGoogle Scholar
  3. 3.
    Grossman R, Kasif S, Moore R, Rocke D, Ullman J Data mining research: opportunities and challenges [Online]. Accessed on May 31, 2010
  4. 4.
    Yang Q, Wu X (2006) 10 challenging problems in data mining research. Int J Inform Technol Decision Making 5(4):597–604CrossRefGoogle Scholar
  5. 5.
    Kantardzic M (2002) Data mining: concepts, models, methods, and algorithms. John Wiley & Sons, Inc. New York, NY, USAGoogle Scholar
  6. 6.
    Han J, Kamber M (2005) Data mining: concepts and techniques. Morgan Kaufman, Publishers Inc. San Francisco, CA, USAGoogle Scholar
  7. 7.
    Atallah M, Bertino E, Elmagarmid A, Ibrahim M, Verykios V (1999) Disclosure limitation of sensitive rules. In: Proceedings of 1999 workshop on knowledge and data engineering exchange, KDEX ‘99, Chicago, 7 November 1999Google Scholar
  8. 8.
    Oliveira S, Zaiane O (2002) Privacy preserving frequent itemset mining, Proceedings of 2nd IEEE international conference on data mining, ICDM’02, Maebashi City, 9–12 December 2002Google Scholar
  9. 9.
    Verykios V, Elmagarmid A, Bertino E, Saygin Y, Dasseni E (2004) Association rule hiding. IEEE Trans Knowledge Data Eng 16(4):434–447CrossRefGoogle Scholar
  10. 10.
    Saygin Y, Verykios V, Clifton C (2001) Using unknowns to prevent discovery of association rules. ACM SIGMOD Records 30(4):45–54CrossRefGoogle Scholar
  11. 11.
    Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. ACM SIGMOD Records 29(2):1–12CrossRefGoogle Scholar
  12. 12.
    Wang S, Maskey R, Jafari A, Hong T (2008) Efficient sanitization of informative association rules. Exp Syst Appl 35(1–2):442–450CrossRefGoogle Scholar
  13. 13.
    Huang H, Wu X, Relue R (2002) Association analysis with one scan of databases. Proceedings second IEEE international conference on data mining, ICDM’02, Maebashi City, 9–12 December 2002, pp 629–632Google Scholar
  14. 14.
    Pavon J, Viana S, Gomez S (2006) Matrix apriori: speeding up the search for frequent patterns. Proceedings 24th IASTED international conference on databases and applications, DBA 2006, Innsbruck, 14–16 February 2006, pp 75–82Google Scholar
  15. 15.
    Yıldız B, Ergenç B (2010) Comparison of two association rule mining algorithms without candidate generation. In: Proceedings 10th IASTED international conference on artificial intelligence and applications, AIA 2010, Innsbruck, 15–17 February 2010, pp 450–457Google Scholar
  16. 16.
    Yıldız B, Ergenç B (2011) Hiding sensitive predictive frequent itemsets, lecture notes in engineering and computer science. In: Proceedings of the international multiconference of engineers and computer scientists 2011, IMECS 2011, Hong Kong, 16–18 March 2011, pp 339–345Google Scholar
  17. 17.
    Ahluwalia M, Gangopadhyay A (2008) Privacy preserving data mining: taxonomy of existing techniques. In: Subramanian R (ed) Computer security, privacy and politics: current issues, challenges and solutions. IRM, New York, pp 70–93CrossRefGoogle Scholar
  18. 18.
    Agrawal D, Aggarwal C (2001) On the design and quantification of privacy preserving data mining algorithms. Proceedings 20th ACM SIGMOD SIGACT-SIGART symposium on principles of database systems, PODS’01, CA, 21–24 May 2001, pp 247–255Google Scholar
  19. 19.
    Agrawal R, Srikant R (2000) Privacy-preserving data mining. ACM SIGMOD Records 29(2):439–450CrossRefGoogle Scholar
  20. 20.
    Liu L, Kantarcioglu M, Thuraisingham B (2008) The applicability of the perturbation based privacy preserving data mining for real-world data. Data Knowledge Eng 65(1):5–21CrossRefGoogle Scholar
  21. 21.
    Lindell Y, Pinkas B (2002) Privacy preserving data mining. J Crytol 15(3):177–206MathSciNetzbMATHGoogle Scholar
  22. 22.
    Pinkas B (2006) Cryptographic techniques for privacy-preserving data mining. ACM SIGKDD Explorations Newslett 4(2):12–19CrossRefGoogle Scholar
  23. 23.
    Bayardo R, Agrawal R (2005) Data privacy through optimal k-anonymization. Proceedings of 21st international conference on data engineering, ICDE’05, Tokyo, 5–8 April 2005, pp 217–228Google Scholar
  24. 24.
    Sweeney L (2002) Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertain, Fuzziness Knowledge-Based Syst 10(5):571–588MathSciNetCrossRefGoogle Scholar
  25. 25.
    Brickell J, Shmatikov V (2008) The cost of privacy: destriction of data-mining utility in anonymized data publishing. Proceedings of 14th ACM SIGKDD international conference on knowledge discovery and data mining, KDD’08, Las Vegas, 24–27 August 2008, pp 70–78Google Scholar
  26. 26.
    Verykios V, Bertino E, Fovino I, Provenza L, Saygin Y, Theodoridis Y (2004) State-of-the-art in privacy preserving data mining. ACM SIGMOD Records 33(1):50–57CrossRefGoogle Scholar
  27. 27.
    Verykios V, Gkoulalas-Divanis A (2008) A survey of association rule hiding methods for privacy. In: Aggarwal C, Yu P (ed) Privacy-preserving data mining: models and algorithms. Springer, New York, pp 267–289CrossRefGoogle Scholar
  28. 28.
    Gkoulalas-Divanis A, Verykios V (2006) An integer programming approach for frequent itemset hiding. Proceedings of 15th ACM international conference on Information and knowledge management, CIKM’06, Virginia, 5–11 November 2006, pp 748–757Google Scholar
  29. 29.
    Gkolalas-Divanis A, Verykios V (2008) Exact knowledge hiding through database extension. IEEE Trans Knowledge Data Eng 21(5):699–713CrossRefGoogle Scholar
  30. 30.
    Mannila H, Toivonen H (1997) Levelwise search and borders of theories in knowledge discovery. Data Mining Knowledge Discov 1(3):241–258CrossRefGoogle Scholar
  31. 31.
    Sun X, Yu P (2005) A border-based approach for hiding sensitive frequent itemsets. Proceedings of 5th IEEE international conference on data mining, ICDM’05, Houston, 27–30 November 2005, pp 426–433Google Scholar
  32. 32.
    Sun X, Yu P (2007) Hiding sensitive frequent itemsets by a border-based approach. J Comput Sci Eng 1(1):74–94CrossRefGoogle Scholar
  33. 33.
    Mousakides G, Verykios V (2008) A max min approach for hiding frequent itemsets. Data Knowledge Eng 65(1):75–89CrossRefGoogle Scholar
  34. 34.
    Boora RK, Shukla R, Misra AK (2009) An improved approach to high level privacy preserving itemset mining. Int J Comput Sci Inform Security 6(3):216–223Google Scholar
  35. 35.
    Mohaisen A, Jho N, Hong D, Nyang D (2010) Privacy preserving association rule mining revisited: privacy enchancement and resource efficiency. IEICE Trans Inform Syst E93(2):315–325CrossRefGoogle Scholar
  36. 36.
    Lin JL, Liu JYC (2007) Privacy preserving itemset mining through fake transactions. Proceedings of 22nd ACM symposium on applied computing, SAC 2007, Seoul, 11–15 March 2007, pp 375–379Google Scholar
  37. 37.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. Proceedings of 20th international conference on very large data bases, VLDB’94, Santiago de Chile, 12–15 September 1994, pp 487–499Google Scholar
  38. 38.
    Cristofor L artool project [Online]. Accessed on May 13, 2010

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Computer EngineeringDokuz Eylul UniversityIzmirTurkey
  2. 2.Department of Computer EngineeringIzmir Institute of TechnologyIzmirTurkey

Personalised recommendations