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Volume 3 Issue 2


Efficient Anonymizations with Enhanced Utility

Jacob Goldberger(a), Tamir Tassa(b),(*)

Transactions on Data Privacy 3:2 (2010) 149 - 175

Abstract, PDF

(a) School of Engineering; Bar-Ilan University; Ramat-Gan; Israel.

(b) Division of Computer Science; The Open University; Ra'anana; Israel.

e-mail:goldbej @eng.biu.ac.il; tamirta @openu.ac.il


Abstract

One of the most well studied models of privacy preservation is k-anonymity. Previous studies of k-anonymization used various utility measures 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 datamining is to deducemethods 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 algorithm that is designed to achieve k-anonymity with high utility, independently of the underlying utility measure. That algorithm is based on a modified version of sequential clustering which is the method of choice in clustering. Experimental comparison with four well known algorithms of k-anonymity show that the sequential clustering algorithm is an efficient algorithm that achieves the best utility results. We also describe a modification of the algorithm that outputs k-anonymizations which respect the additional security measure of l-diversity.

* Corresponding author.

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ISSN: 1888-5063; ISSN (Digital): 2013-1631; D.L.:B-11873-2008; Web Site: http://www.tdp.cat/
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Vicenç Torra, Last modified: 00 : 25 December 12 2014.