  Generative Adversarial Networks (GANs) are deep learning architectures
capable of generating synthetic datasets. Despite producing high-quality
synthetic images, the default GAN has no control over the kinds of images it
generates. The Information Maximizing GAN (InfoGAN) is a variant of the default
GAN that introduces feature-control variables that are automatically learned by
the framework, hence providing greater control over the different kinds of
images produced. Due to the high model complexity of InfoGAN, the generative
distribution tends to be concentrated around the training data points. This is
a critical problem as the models may inadvertently expose the sensitive and
private information present in the dataset. To address this problem, we propose
a differentially private version of InfoGAN (DP-InfoGAN). We also extend our
framework to a distributed setting (DPD-InfoGAN) to allow clients to learn
different attributes present in other clients' datasets in a privacy-preserving
manner. In our experiments, we show that both DP-InfoGAN and DPD-InfoGAN can
synthesize high-quality images with flexible control over image attributes
while preserving privacy.
