A General Survey of Privacy-Preserving Data Mining Models and Algorithms

  • Charu C. Aggarwal
  • Philip S. Yu
Part of the Advances in Database Systems book series (ADBS, volume 34)

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.


Privacy-preserving data mining randomization k-anonymity 


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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Charu C. Aggarwal
    • 1
  • Philip S. Yu
    • 2
  1. 1.IBM Thomas J. Watson Research CenterHawthorneUSA
  2. 2.Department of Computer ScienceUniversity of Illinois at ChicagoChicagoUSA

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