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Incorporating Privacy Concerns in Data Mining on Distributed Data

  • Hui-zhang Shen
  • Ji-di Zhao
  • Ruipu Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4183)

Abstract

Data mining, with its objective to efficiently discover valuable and inherent information from large databases, is particularly sensitive to misuse. Therefore an interesting new direction for data mining research is the development of techniques that incorporate privacy concerns and to develop accurate models without access to precise information in individual data records. 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. We address privacy preserving mining on distributed data in this paper and present an algorithm, based on the combination of probabilistic approach and cryptographic approach, to protect 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 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

  • Hui-zhang Shen
    • 1
  • Ji-di Zhao
    • 1
  • Ruipu Yao
    • 2
  1. 1.Aetna School of ManagementShanghai Jiao Tong UniversityShanghaiP.R. China
  2. 2.School of Information EngineeringTianjin University of CommerceTianjinP.R. China

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