  Synthetic data has been considered a better privacy-preserving alternative to
traditionally sanitized data across various applications. However, a recent
article challenges this notion, stating that synthetic data does not provide a
better trade-off between privacy and utility than traditional anonymization
techniques, and that it leads to unpredictable utility loss and highly
unpredictable privacy gain. The article also claims to have identified a breach
in the differential privacy guarantees provided by PATEGAN and PrivBayes. When
a study claims to refute or invalidate prior findings, it is crucial to verify
and validate the study. In our work, we analyzed the implementation of the
privacy game described in the article and found that it operated in a highly
specialized and constrained environment, which limits the applicability of its
findings to general cases. Our exploration also revealed that the game did not
satisfy a crucial precondition concerning data distributions, which contributed
to the perceived violation of the differential privacy guarantees offered by
PATEGAN and PrivBayes. We also conducted a privacy-utility trade-off analysis
in a more general and unconstrained environment. Our experimentation
demonstrated that synthetic data achieves a more favorable privacy-utility
trade-off compared to the provided implementation of k-anonymization, thereby
reaffirming earlier conclusions.
