A Privacy Preserving Mining Algorithm on Distributed Dataset

  • Shen Hui-zhang
  • Zhao Ji-di
  • Yang Zhong-zhi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


The issue of maintaining privacy in data mining has attracted considerable attention over the last few years. The difficulty lies in the fact that the two metrics for evaluating privacy preserving data mining methods: privacy and accuracy are typically contradictory in nature. This paper addresses privacy preserving mining of association rules on distributed dataset. We present an algorithm, based on a probabilistic approach of distorting transactions in the dataset, which can provide high privacy of individual information and at the same time acquire a high level of accuracy in the mining result. Finally, we present experiment results that validate the algorithm.


Association Rule Minimum Support Frequent Itemsets Markov Chain Model Transition Probability Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shen Hui-zhang
    • 1
  • Zhao Ji-di
    • 1
  • Yang Zhong-zhi
    • 1
  1. 1.Aetna School of ManagementShanghai Jiao Tong UniversityShanghaiP.R. China

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