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Privacy Preserving Mining of Global Association Rules on Distributed Dataset

  • Huizhang Shen
  • Jidi Zhao
  • Ruipu Yao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3975)

Abstract

The issue of maintaining privacy in data mining has attracted considerable attention over the last few years. In this paper, we continue the investigation of the techniques of distorting data in developing data mining techniques without compromising customer privacy and present a privacy preserving data mining algorithm for finding frequent itemsets and mining association rules on distributed data allocated at different sites. Experimental results show that such a distortion approach can provide high privacy of individual information and at the same time acquire a high level of accuracy in the mining result.

Keywords

Association Rule Minimum Support Frequent Itemsets Transition Probability Matrix Privacy Preserve 
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

  • Huizhang Shen
    • 1
  • Jidi Zhao
    • 1
  • Ruipu Yao
    • 2
  1. 1.Institute of System EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Information EngineeringTianjin University of CommerceTianjinChina

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