k-anonymous decision tree induction

Arik Friedman, Assaf Schuster, Ran Wolff

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationKnowledge Discovery in Databases
Subtitle of host publicationPKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
PublisherSpringer Verlag
Pages151-162
Number of pages12
ISBN (Print)3540453741, 9783540453741
DOIs
StatePublished - 2006
Externally publishedYes
Event10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006 - Berlin, Germany
Duration: 18 Sep 200622 Sep 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4213 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006
Country/TerritoryGermany
CityBerlin
Period18/09/0622/09/06

Keywords

  • Decision trees
  • Privacy preserving data mining
  • k-anonymity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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