  This work presents a systematic benchmark of differentially private synthetic
data generation algorithms that can generate tabular data. Utility of the
synthetic data is evaluated by measuring whether the synthetic data preserve
the distribution of individual and pairs of attributes, pairwise correlation as
well as on the accuracy of an ML classification model. In a comprehensive
empirical evaluation we identify the top performing algorithms and those that
consistently fail to beat baseline approaches.
