k-anonymization with minimal loss of information

Aristides Gionis, Tamir Tassa

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


The technique of k-anonymization allows the releasing of databases that contain personal information while ensuring some degree of individual privacy. Anonymization is usually performed by generalizing database entries. We formally study the concept of generalization, and propose two information-theoretic measures for capturing the amount of information that is lost during the anonymization process. Those measures are more general and more accurate than those proposed in [19] and [1]. We study the problem of achieving k-anonymity with minimal loss of information. We prove that it is NP-hard and study polynomial approximations for the optimal solution. Our first algorithm gives an approximation guarantee of O(ln k) - an improvement over the best-known O(k)-approximation of [1]. As the running time of the algorithm is O(n 2k), we also show how to adapt the algorithm of [1] in order to obtain an O(k)-approximation algorithm that is polynomial in both n and k.

Original languageEnglish
Title of host publicationAlgorithms - ESA 2007 - 15th Annual European Symposium, Proceedings
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783540755197
StatePublished - 2007
Event15th Annual European Symposium on Algorithms, ESA 2007 - Eilat, Israel
Duration: 8 Oct 200710 Oct 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4698 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th Annual European Symposium on Algorithms, ESA 2007


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