Differentially Private Generative Adversarial Networks with Model Inversion
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkmodelSimilar Papers 제목 키워드 기반
Differentially Private Data Generative Models
Deep neural networks (DNNs) have recently been widely adopted in various applications, and such success is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a lar…
BIG-bench Machine LearningFederated LearningInference AttackMembership Inference AttackDifferentially Private Generative Models Through Optimal Transport
Although machine learning models trained on massive data have led to breakthroughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data. Generative models train…
Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Although machine learning models trained on massive data have led to breakthroughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data. Generative models train…
Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence
Although machine learning models trained on massive data have led to break-throughs in several areas, their deployment in privacy-sensitive domains remains limited due to restricted access to data. Generative models trai…
DPGOMI: Differentially Private Data Publishing with Gaussian Optimized Model Inversion
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, …