  The promise of tabular generative models is to produce realistic synthetic
data that can be shared and safely used without dangerous leakage of
information from the training set. In evaluating these models, a variety of
methods have been proposed to measure the tendency to copy data from the
training dataset when generating a sample. However, these methods suffer from
either not considering data-copying from a privacy threat perspective, not
being motivated by recent results in the data-copying literature or being
difficult to make compatible with the high dimensional, mixed type nature of
tabular data. This paper proposes a new similarity metric and Membership
Inference Attack called Data Plagiarism Index (DPI) for tabular data. We show
that DPI evaluates a new intuitive definition of data-copying and characterizes
the corresponding privacy risk. We show that the data-copying identified by DPI
poses both privacy and fairness threats to common, high performing
architectures; underscoring the necessity for more sophisticated generative
modeling techniques to mitigate this issue.
