DPGOMI: Differentially Private Data Publishing with Gaussian Optimized
  Model Inversion
Dongjie Chen Sen-ching S. Cheung Chen-Nee Chuah
High-dimensional data are widely used in the era of deep learning with
numerous applications. However, certain data which has sensitive information
are not allowed to be shared without privacy protection. In this paper, we
propose a novel differentially private data releasing method called
Differentially Private Data Publishing with Gaussian Optimized Model Inversion
(DPGOMI) to address this issue. Our approach involves mapping private data to
the latent space using a public generator, followed by a lower-dimensional
DP-GAN with better convergence properties. We evaluate the performance of
DPGOMI on standard datasets CIFAR10 and SVHN. Our results show that DPGOMI
outperforms the standard DP-GAN method in terms of Inception Score, Fr\'echet
Inception Distance, and classification performance, while providing the same
level of privacy. Our proposed approach offers a promising solution for
protecting sensitive data in GAN training while maintaining high-quality
results.