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We show that the canonical approach for training differentially private GANs -- updating the discriminator with differentially private stochastic gradient descent (DPSGD) -- can yield significantly improved results after modifications to training.
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Smooth sensitivity and sampling in private data analysis
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A simple and practical algorithm for differentially private data release
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Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D. Sarwate · 2013
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Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
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The algorithmic foundations of differential privacy
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Deep learning face attributes in the wild
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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How to train a GAN? Tips and tricks to make GANs work
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Unsupervised representation learning with deep convolutional generative adversarial networks
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Wasserstein generative adversarial networks
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GANs trained by a two time-scale update rule converge to a local nash equilibrium
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Semi-supervised knowledge transfer for deep learning from private training data
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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PrivBayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Differentially private data generative models
Qingrong Chen, Chong Xiang, Minhui Xue, Bo Li, Nikita Borisov, Dali Kaafar, and Haojin Zhu · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Privacy-preserving generative deep neural networks support clinical data sharing
Brett K. Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P. Bhavnani, James Brian Byrd, and Casey S. Greene · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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G-PATE: Scalable differentially private data generator via private aggregation of teacher discriminators
Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl Gunter, and Bo Li · 2021
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Private post-GAN boosting
Marcel Neunhoeffer, Steven Wu, and Cynthia Dwork · 2021
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Benchmarking differentially private synthetic data generation algorithms
Yuchao Tao, Ryan McKenna, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 2021
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Differentially private learning needs better features (or much more data)
Florian Tramèr and Dan Boneh · 2021
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Differentially private normalizing flows for privacy-preserving density estimation
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Private selection from private candidates
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Graphical-model based estimation and inference for differential privacy
Ryan McKenna, Daniel Sheldon, and Gerome Miklau · 2019
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Rényi differential privacy of the sampled Gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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DP-CGAN: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Subsampled Rényi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
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GS-WGAN: A gradient-sanitized approach for learning differentially private generators
Dingfan Chen, Tribhuvanesh Orekondy, and Mario Fritz · 2020
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DataLens: Scalable privacy preserving training via gradient compression and aggregation
Boxin Wang, Fan Wu, Yunhui Long, Luka Rimanic, Ce Zhang, and Bo Li · 2021
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Opacus: User-friendly differential privacy library in PyTorch
Ashkan Yousefpour, Igor Shilov, Alexandre Sablayrolles, Davide Testuggine, Karthik Prasad, Mani Malek, John Nguyen, Sayan Gosh, Akash Bharadwaj, Jessica Zhao, Graham Cormode, and Ilya Mironov · 2021
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Large-scale differentially private BERT
Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi · 2022
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Private set generation with discriminative information
Dingfan Chen, Raouf Kerkouche, and Mario Fritz · 2022
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Unlocking high-accuracy differentially private image classification through scale
Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle · 2022
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Differentially private diffusion models
Tim Dockhorn, Tianshi Cao, Arash Vahdat, and Karsten Kreis · 2022
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Differentially private data generation needs better features
Frederik Harder, Milad Jalali Asadabadi, Danica J. Sutherland, and Mijung Park · 2022
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Large language models can be strong differentially private learners
Xuechen Li, Florian Tramèr, Percy Liang, and Tatsunori Hashimoto · 2022
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The role of adaptive optimizers for honest private hyperparameter selection
Shubhankar Mohapatra, Sajin Sasy, Xi He, Gautam Kamath, and Om Thakkar · 2022
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StyleGAN-XL: Scaling StyleGAN to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
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Hermite polynomial features for private data generation
Margarita Vinaroz, Mohammad-Amin Charusaie, Frederik Harder, Kamil Adamczewski, and Mi Jung Park · 2022
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Differentially private fine-tuning of language models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A. Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, and Huishuai Zhang · 2022
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Scaling up GANs for text-to-image synthesis
Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park · 2023
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How to dp-fy ML: A practical guide to machine learning with differential privacy
Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Guha Thakurta · 2023
Closest in time.