Preventing equivalence attacks in updated, anonymized data

Authors:
Yeye He;Siddharth Barman;Jeffrey F. Naughton
Affiliations:
Computer Science Department, University of Wisconsin-Madison, USA;Computer Science Department, University of Wisconsin-Madison, USA;Computer Science Department, University of Wisconsin-Madison, USA
Venue:
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Year:
2011

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Abstract

In comparison to the extensive body of existing work considering publish-once, static anonymization, dynamic anonymization is less well studied. Previous work, most notably m-invariance, has made considerable progress in devising a scheme that attempts to prevent individual records from being associated with too few sensitive values. We show, however, that in the presence of updates, even an m-invariant table can be exploited by a new type of attack we call the "equivalence-attack." To deal with the equivalence attack, we propose a graph-based anonymization algorithm that leverages solutions to the classic "min-cut/max-flow" problem, and demonstrate with experiments that our algorithm is efficient and effective in preventing equivalence attacks.