  Generative models have been found effective for data synthesis due to their
ability to capture complex underlying data distributions. The quality of
generated data from these models is commonly evaluated by visual inspection for
image datasets or downstream analytical tasks for tabular datasets. These
evaluation methods neither measure the implicit data distribution nor consider
the data privacy issues, and it remains an open question of how to compare and
rank different generative models. Medical data can be sensitive, so it is of
great importance to draw privacy concerns of patients while maintaining the
data utility of the synthetic dataset. Beyond the utility evaluation, this work
outlines two metrics called Similarity and Uniqueness for sample-wise
assessment of synthetic datasets. We demonstrate the proposed notions with
several state-of-the-art generative models to synthesise Cystic Fibrosis (CF)
patients' electronic health records (EHRs), observing that the proposed metrics
are suitable for synthetic data evaluation and generative model comparison.
