On the Privacy Preserving Mining Association Rules by using Randomization 


Vol. 14,  No. 5, pp. 439-452, Aug.  2007
10.3745/KIPSTC.2007.14.5.439


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  Abstract

We study on the privacy preserving data mining, PPDM for short, by using randomization. The theoretical PPDM based on the secure multi-party computation techniques is not practical for its computational inefficiency. So we concentrate on a practical PPDM, especially randomization technique. We survey various privacy measures and study on the privacy preserving mining of association rules by using randomization. We propose a new randomization operator, binomial selector, for privacy preserving technique of association rule mining. A binomial selector is a special case of a select-a-size operator by Evfimievski et al.[3]. Moreover we present some simulation results of detecting an appropriate parameter for a binomial selector. The randomization by a so-called cut-and-paste method in [3] is not efficient and has high variances on recovered support values for large item-sets. Our randomization by a binomial selector make up for this defects of cut-and-paste method.

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[IEEE Style]

J. S. Kang, S. H. Cho, O. Y. Yi, D. W. Hong, "On the Privacy Preserving Mining Association Rules by using Randomization," The KIPS Transactions:PartC, vol. 14, no. 5, pp. 439-452, 2007. DOI: 10.3745/KIPSTC.2007.14.5.439.

[ACM Style]

Ju Sung Kang, Sung Hoon Cho, Ok Yeon Yi, and Do Won Hong. 2007. On the Privacy Preserving Mining Association Rules by using Randomization. The KIPS Transactions:PartC, 14, 5, (2007), 439-452. DOI: 10.3745/KIPSTC.2007.14.5.439.

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