@inproceedings{811cbc75c6be434b804c4f4622e699b0,
title = "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.",
keywords = "Decision trees, Privacy preserving data mining, k-anonymity",
author = "Arik Friedman and Assaf Schuster and Ran Wolff",
year = "2006",
doi = "10.1007/11871637_18",
language = "English",
isbn = "3540453741",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "151--162",
booktitle = "Knowledge Discovery in Databases",
address = "Germany",
note = "10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006 ; Conference date: 18-09-2006 Through 22-09-2006",
}