Preventing Equivalence Attacks in Updated, Anonymized Data

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 “equivalenceattack.” 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.