Differentially Private Generative Adversarial Networks with Model Inversion. (arXiv:2201.03139v2 [cs.LG] UPDATED)

To protect sensitive data in training a Generative Adversarial Network (GAN),
the standard approach is to use differentially private (DP) stochastic gradient
descent method in which controlled noise is added to the gradients. The quality
of the output synthetic samples can be adversely affected and the training of
the network may not even converge in the presence of these noises. We propose
Differentially Private Model Inversion (DPMI) method where the private data is
first mapped to the latent space via …

adversarial generative adversarial networks networks