DP-CGAN: Differentially Private Synthetic Data and Label Generation

By Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten. 2019

282 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Generative Models for Effective ML on Private, Decentralized Datasets (2019, background): “Prior work has looked at training GANs with DP in a centralized setting (Torkzadehmahani et al., 2019; Xie et al., 2018; Zhang et al., 2018; Frigerio et al., 2019; Beaulieu-Jones et al., 2018; Esteban et al., 2017).”
Large Language Models Can Be Strong Differentially Private Learners (2021, background): “Previous work employed generative adversarial networks and focused primarily on image or tabular datasets (Torkzadehmahani et al., 2019; Neunhoeffer et al., 2020; Chen et al., 2020; Torfi et al., 2020).”
Machine Learning for Synthetic Data Generation: a Review (2023, background): “…and HGDP datasets synthpop [29] healthcare proper synthesis Statistical hypothesis testing SD2011 CycleGAN [30] vision GAN CNN pix2pix DP-CGAN [31] vision GAN deep CGAN MNIST BigGANs [32] vision GAN large scale GAN ImageNet VideoDiff [33] vision diffusion CNN BAIR Robot Pushing, Kinetics-600…”
Federated Adversarial Debiasing for Fair and Transferable Representations (2021, background): “During the learning of the adversarial discriminator, injecting predefined noise is known to be effective to defend such attacks [41].”