Differentially Private Data Generation Needs Better Features. (arXiv:2205.12900v2 [stat.ML] UPDATED)

Training even moderately-sized generative models with differentially-private
stochastic gradient descent (DP-SGD) is difficult: the required level of noise
for reasonable levels of privacy is simply too large. We advocate instead
building off a good, relevant representation on an informative public dataset,
then learning to model the private data with that representation. In
particular, we minimize the maximum mean discrepancy (MMD) between private
target data and a generator's distribution, using a kernel based on perceptual
features learned from a public dataset. …

data features private data