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Machine learning has the potential to assist many communities in using thelarge datasets that are becoming more and more available. Unfortunately, muchof that potential is not being realized because it would require sharing datain a way that compromises privacy. In order to overcome this hurdle, severalmethods have been proposed that generate synthetic data while preserving theprivacy of the real data. In this paper we consider a key characteristic thatsynthetic data should have in order to be useful for machine learningresearchers - the relative performance of two algorithms (trained and tested)on the synthetic dataset should be the same as their relative performance (whentrained and tested) on the original dataset.

Measuring the quality of Synthetic data for use in competitions