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Corporations are retaining ever-larger corpuses of personal data; thefrequency or breaches and corresponding privacy impact have been risingaccordingly. One way to mitigate this risk is through use of anonymized data,limiting the exposure of individual data to only where it is absolutely needed.This would seem particularly appropriate for data mining, where the goal isgeneralizable knowledge rather than data on specific individuals. In practice,corporate data miners often insist on original data, for fear that they might"miss something" with anonymized or differentially private approaches. Thispaper provides a theoretical justification for the use of anonymized data.Specifically, we show that a support vector classifier trained on anatomizeddata satisfying l-diversity should be expected to do as well as on the originaldata. Anatomy preserves all data values, but introduces uncertainty in themapping between identifying and sensitive values, thus satisfying l-diversity.The theoretical effectiveness of the proposed approach is validated usingseveral publicly available datasets, showing that we outperform the state ofthe art for support vector classification using training data protected byk-anonymity, and are comparable to learning on the original data.

Statistical Learning Theory Approach for Data Classification with l-diversity