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Differential privacy provides strong privacy guarantees for machine learningapplications. Much recent work has been focused on developing differentiallyprivate models, however there has been a gap in other stages of the machinelearning pipeline, in particular during the preprocessing phase. Ourcontributions are twofold: we adapt a privacy violation detection frameworkbased on statistical methods to empirically measure privacy levels of machinelearning pipelines, and apply the newly created framework to show thatresampling techniques used when dealing with imbalanced datasets cause theresultant model to leak more privacy. These results highlight the need fordeveloping private preprocessing techniques.

Statistical Privacy Guarantees of Machine Learning Preprocessing Techniques