  Differentially private synthetic data is a promising alternative for
sensitive data release. Many differentially private generative models have been
proposed in the literature. Unfortunately, they all suffer from the low utility
of the synthetic data, particularly for images of high resolutions. Here, we
propose DPAF, an effective differentially private generative model for
high-dimensional image synthesis. Different from the prior private stochastic
gradient descent-based methods that add Gaussian noises in the backward phase
during the model training, DPAF adds a differentially private feature
aggregation in the forward phase, bringing advantages, including the reduction
of information loss in gradient clipping and low sensitivity for the
aggregation. Moreover, as an improper batch size has an adverse impact on the
utility of synthetic data, DPAF also tackles the problem of setting a proper
batch size by proposing a novel training strategy that asymmetrically trains
different parts of the discriminator. We extensively evaluate different methods
on multiple image datasets (up to images of 128x128 resolution) to demonstrate
the performance of DPAF.
