K-anonymization revisited

Aristides Gionis, Arnon Mazza, Tamir Tassa

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

73 Scopus citations


In this paper we introduce new notions of k-type anonymizations. Those, notions achieve similar privacy goals as those aimed by Sweenie and Samarati when proposing the concept of k-anonymization: an adversary who knows the public data of an individual cannot link that individual to less than k records in the anonymized table. Every anonymized table that satisfies k-anonymity complies also with the anonymity constraints dictated by the new notions, but the converse is not necessarily true. Thus, those new notions allow generalized tables that may offer higher utility than k-anonymized tables, while still preserving the required privacy constraints. We discuss and compare the new anonymization concepts, which we call (1, k)-. (k, k)- and global (1, k)-anonymizations, according to several utility measures. We propose a collection of agglomerative algorithms for the problem of finding such anonymizations with high utility, and demonstrate the usefulness of our definitions and our algorithms through extensive experimental evaluation on real and synthetic datasets.

Original languageEnglish
Title of host publicationProceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Number of pages10
StatePublished - 1 Oct 2008
Externally publishedYes
Event2008 IEEE 24th International Conference on Data Engineering, ICDE'08 - Cancun, Mexico
Duration: 7 Apr 200812 Apr 2008

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627


Conference2008 IEEE 24th International Conference on Data Engineering, ICDE'08

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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