  Devising procedures for auditing generative model privacy-utility tradeoff is
an important yet unresolved problem in practice. Existing works concentrates on
investigating the privacy constraint side effect in terms of utility
degradation of the train on synthetic, test on real paradigm of synthetic data
training. We push such understanding on privacy-utility tradeoff to next level
by observing the privacy deregulation side effect on synthetic training data
utility. Surprisingly, we discover the Utility Recovery Incapability of
DP-CTGAN and PATE-CTGAN under privacy deregulation, raising concerns on their
practical applications. The main message is Privacy Deregulation does NOT
always imply Utility Recovery.
