Efficient anonymizations with enhanced utility

Jacob Goldberger, Tamir Tassa

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

9 Scopus citations

Abstract

The k-anonymization method is a commonly used privacy-preserving technique. Previous studies used various measures of utility that aim at enhancing the correlation between the original public data and the generalized public data. We, bearing in mind that a primary goal in releasing the anonymized database for data mining is to deduce methods of predicting the private data from the public data, propose a new information-theoretic measure that aims at enhancing the correlation between the generalized public data and the private data. Such a measure significantly enhances the utility of the released anonymized database for data mining. We then proceed to describe a new and highly efficient algorithm that is designed to achieve k-anonymity with high utility. That algorithm is based on a modified version of sequential clustering which is the method of choice in clustering, and it is independent of the underlying measure of utility.

Original languageEnglish
Title of host publicationICDM Workshops 2009 - IEEE International Conference on Data Mining
Pages106-113
Number of pages8
DOIs
StatePublished - 1 Dec 2009
Externally publishedYes
Event2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009 - Miami, FL, United States
Duration: 6 Dec 20096 Dec 2009

Publication series

NameICDM Workshops 2009 - IEEE International Conference on Data Mining

Conference

Conference2009 IEEE International Conference on Data Mining Workshops, ICDMW 2009
Country/TerritoryUnited States
CityMiami, FL
Period6/12/096/12/09

Keywords

  • Data privacy
  • K-anonymity
  • Mutual information

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