k-Anonymous Decision Tree Induction
Arik Friedman, Assaf Schuster, Ran Wolff · Lecture notes in computer science · 2006
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.