Providing k-anonymity in data mining

Arik Friedman, Ran Wolff, Assaf Schuster

Research output: Contribution to journalArticlepeer-review


In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in the learning examples. Our extension provides a tool that measures the amount of anonymity retained during data mining. We show that our model can be applied to various data mining problems, such as classification, association rule mining and clustering. We describe two data mining algorithms which exploit our extension to guarantee they will generate only k-anonymous output, and provide experimental results for one of them. Finally, we show that our method contributes new and efficient ways to anonymize data and preserve patterns during anonymization.

Original languageEnglish
Pages (from-to)789-804
Number of pages16
JournalVLDB Journal
Issue number4
StatePublished - Jul 2008
Externally publishedYes

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture


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