k-anonymous decision tree induction

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. © Springer-Verlag Berlin Heidelberg 2006.