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http://dx.doi.org/10.3745/KIPSTC.2007.14-C.5.439

On the Privacy Preserving Mining Association Rules by using Randomization  

Kang, Ju-Sung (국민대학교 수학과)
Cho, Sung-Hoon (누리솔루션)
Yi, Ok-Yeon (국민대학교 수학과)
Hong, Do-Won (한국전자통신연구원)
Publication Information
The KIPS Transactions:PartC / v.14C, no.5, 2007 , pp. 439-452 More about this Journal
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.
Keywords
Data mining; PPDM; Randomization; Association rule mining; Privacy measure;
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