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