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Use HypE to Hide Association Rules by Adding Items

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  • Peng Cheng
  • Chun-Wei Lin
  • Jeng-Shyang Pan

Abstract

During business collaboration, partners may benefit through sharing data. People may use data mining tools to discover useful relationships from shared data. However, some relationships are sensitive to the data owners and they hope to conceal them before sharing. In this paper, we address this problem in forms of association rule hiding. A hiding method based on evolutionary multi-objective optimization (EMO) is proposed, which performs the hiding task by selectively inserting items into the database to decrease the confidence of sensitive rules below specified thresholds. The side effects generated during the hiding process are taken as optimization goals to be minimized. HypE, a recently proposed EMO algorithm, is utilized to identify promising transactions for modification to minimize side effects. Results on real datasets demonstrate that the proposed method can effectively perform sanitization with fewer damages to the non-sensitive knowledge in most cases.

Suggested Citation

  • Peng Cheng & Chun-Wei Lin & Jeng-Shyang Pan, 2015. "Use HypE to Hide Association Rules by Adding Items," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-19, June.
    • Handle: RePEc:plo:pone00:0127834
    DOI: 10.1371/journal.pone.0127834
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    References listed on IDEAS

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    1. Beume, Nicola & Naujoks, Boris & Emmerich, Michael, 2007. "SMS-EMOA: Multiobjective selection based on dominated hypervolume," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1653-1669, September.
    2. Syam Menon & Sumit Sarkar, 2007. "Minimizing Information Loss and Preserving Privacy," Management Science, INFORMS, vol. 53(1), pages 101-116, January.
    3. Syam Menon & Sumit Sarkar & Shibnath Mukherjee, 2005. "Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns," Information Systems Research, INFORMS, vol. 16(3), pages 256-270, September.
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    1. Syam Menon & Abhijeet Ghoshal & Sumit Sarkar, 2022. "Modifying Transactional Databases to Hide Sensitive Association Rules," Information Systems Research, INFORMS, vol. 33(1), pages 152-178, March.

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