• Journal of Digital Convergence
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• v.16 no.10
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• pp.445-452
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• 2018

In a world where everything in life is being digitized, the amount of data is increasing exponentially. These data are processed into new data through collection and analysis. New data is used for a variety of purposes in hospitals, finance, and businesses. However, since existing data contains sensitive information of individuals, there is a fear of personal privacy exposure during collection and analysis. As a solution, there is privacy-preserving data mining (PPDM) technology. PPDM is a method of extracting useful information from data while preserving privacy. In this paper, we investigate PPDM and analyze various measures for evaluating the privacy and utility of data.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.983-986
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• 2005

최근 들어서 데이터 마이닝은 마케팅, 시장 분석, 사업전략 및 도시계획 수립 등 다양한 분야에서 폭넓게 활용되고 있으며, 새로운 분야로 그 활용 영역을 넓혀가고 있다. 하지만 데이터 마이닝은 그 과정에서 데이터 소유자들의 프라이버시가 침해될 수 있는 문제를 내포하고 있으며, 최근에는 이러한 문제를 해결하고자 하는 노력들이 나타나고 있다. 본 논문에서는 데이터 마이닝에서 이러한 문제를 해결하기 위한 프라이버시 보호 기술들에 대해서 살펴보고 각 방법의 특징에 대해서 기술한다. 특히, 안전한 다자간 계산(Secure multiparty computation)에 기반한 암호학적 프라이버시 보호 기술과 그 활용 가능성에 대해서도 기술한다.

• The KIPS Transactions:PartC
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• v.14C no.5
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• pp.439-452
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• 2007

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.