Bottom-Up Cell Suppression that Preserves the Missing-at-random Condition

This paper proposes a cell-suppression based k-anonymization method which keeps minimal the loss of utility. The proposed method uses the Kullback-Leibler (KL) divergence as a utility measure derived from the notions developed in the literature of incomplete data analysis, including the missing-at-random (MAR) condition. To be more specific, we plug the KL divergence into an bottom-up, greedy procedure for a local recoding k-anonymization as a cost function which is efficiently computed. We focus on classification datasets and experimental results exhibit that the proposed method yields a small degradation of classification performance when combined with naive Bayes classifiers.