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Preventing equivalence attacks in updated, anonymized data

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3 Author(s)
Yeye He ; Comput. Sci. Dept., Univ. of Wisconsin-Madison, Madison, WI, USA ; Barman, S. ; Naughton, J.F.

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.

Published in:

Data Engineering (ICDE), 2011 IEEE 27th International Conference on

Date of Conference:

11-16 April 2011