  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 a public generator, followed by a
lower-dimensional DP-GAN with better convergent properties. Experimental
results on standard datasets CIFAR10 and SVHN as well as on a facial landmark
dataset for Autism screening show that our approach outperforms the standard
DP-GAN method based on Inception Score, Fr\'echet Inception Distance, and
classification accuracy under the same privacy guarantee.
