| Title | DP-CGAN: Differentially Private Synthetic Data and Label Generation |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Torkzadehmahani, Reihaneh, Kairouz, Peter, Paten, Benedict |
| Conference Name | 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
| Date Published | jun |
| Keywords | AI, Data models, data privacy, differentially private conditional GAN training framework, differentially private synthetic data, DP-CGAN, Gallium nitride, GAN models, generative adversarial networks, Generators, Human Behavior, human factors, label generation, learning (artificial intelligence), MNIST dataset, original sensitive datasets, privacy, pubcrawl, Renyi differential privacy accountant, research communities, resilience, Resiliency, Scalability, single-digit epsilon parameter, spent privacy budget, Training, training dataset |
| Abstract | Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic data including images, especially for research communities that cannot use original sensitive datasets because they are not publicly accessible. One of the main challenges in this area is to preserve the privacy of individuals who participate in the training of the GAN models. To address this challenge, we introduce a Differentially Private Conditional GAN (DP-CGAN) training framework based on a new clipping and perturbation strategy, which improves the performance of the model while preserving privacy of the training dataset. DP-CGAN generates both synthetic data and corresponding labels and leverages the recently introduced Renyi differential privacy accountant to track the spent privacy budget. The experimental results show that DP-CGAN can generate visually and empirically promising results on the MNIST dataset with a single-digit epsilon parameter in differential privacy. |
| DOI | 10.1109/CVPRW.2019.00018 |
| Citation Key | torkzadehmahani_dp-cgan_2019 |
DP-CGAN: Differentially Private Synthetic Data and Label Generation