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Preserving privacy in association rule mining with bloom filters

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Abstract

Privacy preserving association rule mining has been an active research area since recently. To this problem, there have been two different approaches—perturbation based and secure multiparty computation based. One drawback of the perturbation based approach is that it cannot always fully preserve individual’s privacy while achieving precision of mining results. The secure multiparty computation based approach works only for distributed environment and needs sophisticated protocols, which constrains its practical usage. In this paper, we propose a new approach for preserving privacy in association rule mining. The main idea is to use keyed Bloom filters to represent transactions as well as data items. The proposed approach can fully preserve privacy while maintaining the precision of mining results. The tradeoff between mining precision and storage requirement is investigated. We also propose δ-folding technique to further reduce the storage requirement without sacrificing mining precision and running time.

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Authors and Affiliations

  1. School of Math, Physics and Information Technology, James Cook University, Townsville, Queensland, 4811, Australia

    Ling Qiu

  2. School of Information Systems, Singapore Management University, Singapore, 178902, Singapore

    Yingjiu Li

  3. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, 28223, USA

    Xintao Wu

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  1. Ling Qiu
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  2. Yingjiu Li
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  3. Xintao Wu
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Correspondence to Ling Qiu.

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Qiu, L., Li, Y. & Wu, X. Preserving privacy in association rule mining with bloom filters. J Intell Inf Syst 29, 253–278 (2007). https://doi.org/10.1007/s10844-006-0018-8

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  • DOI: https://doi.org/10.1007/s10844-006-0018-8

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