Robin Hood and Matthew Effects: Differential Privacy Has Disparate Impact on Synthetic Data. (arXiv:2109.11429v3 [cs.LG] UPDATED)

Generative models trained with Differential Privacy (DP) can be used to
generate synthetic data while minimizing privacy risks. We analyze the impact
of DP on these models vis-a-vis underrepresented classes/subgroups of data,
specifically, studying: 1) the size of classes/subgroups in the synthetic data,
and 2) the accuracy of classification tasks run on them. We also evaluate the
effect of various levels of imbalance and privacy budgets. Our analysis uses
three state-of-the-art DP models (PrivBayes, DP-WGAN, and PATE-GAN) and shows
that …

data differential privacy impact lg privacy robin synthetic data