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Privacy Preserving Data Utility Mining Using Perturbation

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Distributed Computing and Internet Technology (ICDCIT 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10722))

Abstract

Data Mining is a field of research dealing with the automatic discovery of knowledge within databases. Recent advances in data mining has increased the disclosure risks that one may encounter when releasing data to outside parties. Privacy preserving data mining (PPDM) deals with protecting the privacy of individual data or sensitive knowledge without sacrificing the utility of the data. Privacy Preserving Utility Mining (PPUM) is an extension of PPDM where the quantity as well as the utility are taken care of. Perturbation is a technique which modifies the contents of database with constraints and satisfies the privacy policies of the data holder. A Fast Perturbation using Frequency Count (FPUFC) algorithm is proposed to hide all sensitive high utility itemsets. The performance of proposed algorithm were compared with that of the existing algorithm, Fast Perturbation using Tree and Table Structures (FPUTT). FPUFC shows better performance by taking lesser execution time compared to FPUTT.

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References

  1. Lin, J.C.W., Wu, T.Y., Fournier - Viger, P., Lin, G., Zhan, J., Voznak, M.: Fast algorithms for hiding sensitive high-utility itemsets in privacy-preserving utility mining. Eng. Appl. Artif. Intell. 55(C), 269–284 (2016)

    Article  Google Scholar 

  2. Yeh, J.S., Hsu, P.C.: HHUIF and MSICF: novel algorithms for privacy preserving utility mining. Expert Syst. Appl. 37(7), 4779–4786 (2010)

    Article  Google Scholar 

  3. Verykios, V.S., Elmagarmid, A.K., Bertino, E., Saygin, Y., Dasseni, E.: Association rule hiding. IEEE Trans. Knowl. Data Eng. 16(4), 434–447 (2004)

    Article  Google Scholar 

  4. Dehkordi, M.N., Badie, K., Zadeh, A.K.: A novel method for privacy preservation in association rule mining based on genetic algorithms. J. Softw. 4, 555–562 (2009)

    Article  Google Scholar 

  5. Oliveira, S.R., Zaiane, O.R.: Privacy preserving frequent itemset mining. In: Proceedings of IEEE ICDM Workshop On Privacy, Security and Data Mining, pp. 45–54 (2002)

    Google Scholar 

  6. Yun, U., Kim, J.: Fast perturbation algorithm using tree structure for privacy preserving utility mining. Expert Syst. Appl. 42, 1149–1165 (2015). ELSEVIER

    Article  Google Scholar 

  7. Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: UP–growth: an efficient algorithm for high utility itemset mining. In: Proceedings of 16th ACM SIGKDD, pp. 253–262 (2010)

    Google Scholar 

  8. Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Syst. Appl. 38(6), 7419–7424 (2011). ELSEVIER

    Article  Google Scholar 

  9. Lin, C.W., Zhang, B., Yang, K.T., Hong, T.P.: Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms. Sci. World J. 2014, 1–13 (2014)

    Google Scholar 

  10. Liu, M., Qu, J.: Mining high utility itemsets without candidate generation. In: ACM International Conference on Information and Knowledge Management, pp. 55–64 (2012)

    Google Scholar 

  11. Yao, H., Hailton, H.J.: Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3), 603–626 (2006)

    Article  Google Scholar 

  12. Liu, Y., Liao, W., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets. In: Ho, T.B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005). https://doi.org/10.1007/11430919_79

    Chapter  Google Scholar 

  13. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 547–559 (1994)

    Google Scholar 

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Authors and Affiliations

  1. College of Engineering Trivandrum, Thiruvananthapuram, India

    Joseph Jisna & A. Salim

Authors
  1. Joseph Jisna
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  2. A. Salim
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Correspondence to Joseph Jisna .

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Editors and Affiliations

  1. University of Hyderabad, Hyderabad, India

    Atul Negi

  2. University of Cincinnati, Cincinnati, Ohio, USA

    Raj Bhatnagar

  3. IBM Thomas J. Watson Research Center, Yorktown Heights, New York, USA

    Laxmi Parida

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Jisna, J., Salim, A. (2018). Privacy Preserving Data Utility Mining Using Perturbation. In: Negi, A., Bhatnagar, R., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2018. Lecture Notes in Computer Science(), vol 10722. Springer, Cham. https://doi.org/10.1007/978-3-319-72344-0_7

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  • DOI: https://doi.org/10.1007/978-3-319-72344-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72343-3

  • Online ISBN: 978-3-319-72344-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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