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)


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.


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


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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.

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