  Synthetic data has been advertised as a silver-bullet solution to
privacy-preserving data publishing that addresses the shortcomings of
traditional anonymisation techniques. The promise is that synthetic data drawn
from generative models preserves the statistical properties of the original
dataset but, at the same time, provides perfect protection against privacy
attacks. In this work, we present the first quantitative evaluation of the
privacy gain of synthetic data publishing and compare it to that of previous
anonymisation techniques.
  Our evaluation of a wide range of state-of-the-art generative models
demonstrates that synthetic data either does not prevent inference attacks or
does not retain data utility. In other words, we empirically show that
synthetic data does not provide a better tradeoff between privacy and utility
than traditional anonymisation techniques.
  Furthermore, in contrast to traditional anonymisation, the privacy-utility
tradeoff of synthetic data publishing is hard to predict. Because it is
impossible to predict what signals a synthetic dataset will preserve and what
information will be lost, synthetic data leads to a highly variable privacy
gain and unpredictable utility loss. In summary, we find that synthetic data is
far from the holy grail of privacy-preserving data publishing.
