• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.5
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• pp.149-160
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• 2011

Technique based on the notions of anonymity is one of several ways to achieve the goal of privacy and it transforms the original data into the micro data by some group based methods. The first notion of group based method is ${\kappa}$-anonymity, and it is enhanced by the notions of ${\ell}$-diversity and t-closeness. Since there is the natural tradeoff between privacy and data utility, the development of practical anonymization algorithms is not a simple work and there is still no noticeable algorithm which achieves some combined anonymity conditions. In this paper, we provides a comparative analysis of previous anonymity and accuracy measures. Moreover we propose an algorithm to achieve ${\ell}$-diversity by the block merging method from a micro-data achieving ${\kappa}$-anonymity.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.1131-1134
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• 2007

본 논문에서는 랜덤대치(random substitution) 기법에 대하여 심도 있는 분석을 실시한다. 랜덤대치 기법의 효율적인 구현을 위하여 데이터 재구축(reconstruction) 과정에서 필요로 하는 역행렬을 구하는 공식을 제시한다. 또한, 랜덤대치에 사용되는 다양한 파라미터들의 의미를 실험적으로 밝혀내며, 정확도와 프라이버시를 합리적으로 측정할 수 있는 새로운 측도(measure)들을 제안한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.4
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• pp.53-68
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• 2008

We study on the practical privacy-preserving techniques by matrix-based randomization approach. We clearly examine the relationship between the two parameters associated with the measure of privacy breach and the condition number of matrix in order to achieve the optimal transition matrix. We propose a simple formula for efficiently calculating the inverse of transition matrix which are needed in the re-construction process of random substitution algorithm, and deduce some useful connections among standard error and another parameters by obtaining condition numbers according to norms of matrix and the expectation and variance of the transformed data. Moreover we give some experimental results about our theoretical expressions by implementing random substitution algorithm.