k-Anonymous Decision Tree Induction

  • Arik Friedman
  • Assaf Schuster
  • Ran Wolff
Conference paper

DOI: 10.1007/11871637_18

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)
Cite this paper as:
Friedman A., Schuster A., Wolff R. (2006) k-Anonymous Decision Tree Induction. In: Fürnkranz J., Scheffer T., Spiliopoulou M. (eds) Knowledge Discovery in Databases: PKDD 2006. PKDD 2006. Lecture Notes in Computer Science, vol 4213. Springer, Berlin, Heidelberg

Abstract

In this paper we explore an approach to privacy preserving data mining that relies on the k-anonymity model. The k-anonymity model guarantees that no private information in a table can be linked to a group of less than k individuals. We suggest extended definitions of k-anonymity that allow the k-anonymity of a data mining model to be determined. Using these definitions, we present decision tree induction algorithms that are guaranteed to maintain k-anonymity of the learning examples. Experiments show that embedding anonymization within the decision tree induction process provides better accuracy than anonymizing the data first and inducing the tree later.

Keywords

k-anonymity privacy preserving data mining decision trees 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arik Friedman
    • 1
  • Assaf Schuster
    • 1
  • Ran Wolff
    • 1
  1. 1.Computer Science Dept.Technion – Israel Institute of Technology 

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