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Training even moderately-sized generative models with differentially-private stochastic gradient descent (DP-SGD) is difficult: the required level of noise for reasonable levels of privacy is simply too large.
Adaptive estimation of a quadratic functional by model selection
Beatrice Laurent and Pascal Massart · 2000
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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A Hilbert space embedding for distributions
A. Smola, A. Gretton, L. Song, and B. Schölkopf · 2007
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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MNIST handwritten digit database
Yann LeCun and Corinna Cortes · 2010
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Optimistic rates for learning with a smooth loss, 2010
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
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Differentially private data release through multidimensional partitioning
Yonghui Xiao, Li Xiong, and Chun Yuan · 2010
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Differentially private data release for data mining
Noman Mohammed, Rui Chen, Benjamin C.M. Fung, and Philip S. Yu · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
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Universality, characteristic kernels and rkhs embedding of measures
Bharath K Sriperumbudur, Kenji Fukumizu, and Gert RG Lanckriet · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank Mcsherry · 2012
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Concentration inequalities: A nonasymptotic theory of independence
S. Boucheron, G. Lugosi, and P. Massart · 2013
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Kernel embeddings of conditional distributions: A unified kernel framework for nonparametric inference in graphical models
Le Song, Kenji Fukumizu, and Arthur Gretton · 2013
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Generative adversarial networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Priview: practical differentially private release of marginal contingency tables
Wahbeh Qardaji, Weining Yang, and Ninghui Li · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Differentially private high-dimensional data publication via sampling-based inference
Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu · 2015
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Training generative neural networks via Maximum Mean Discrepancy optimization
Gintare Karolina Dziugaite, Daniel M. Roy, and Zoubin Ghahramani · 2015
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Generative moment matching networks
Yujia Li, Kevin Swersky, and Richard S. Zemel · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Learning with privacy at scale, 2017
Differential Privacy Team, Apple · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local Nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar · 2017
Cited alongside, same era.
DP-EM: Differentially Private Expectation Maximization
Mijung Park, James Foulds, Kamalika Choudhary, and Max Welling · 2017
Achieving differential privacy and fairness in logistic regression
Depeng Xu, Shuhan Yuan, and Xintao Wu · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 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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Differentially private and fair classification via calibrated functional mechanism
Jiahao Ding, Xinyue Zhang, Xiaohuan Li, Junyi Wang, Rong Yu, and Miao Pan · 2020
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pytorch-fid: FID Score for PyTorch
Maximilian Seitzer · 2020
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Don’t generate me: Training differentially private generative models with sinkhorn divergence
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Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Privbayes: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
Cited alongside, same era.
Differentially private data publishing and analysis: A survey
T. Zhu, G. Li, W. Zhou, and P. S. Yu · 2017
Cited alongside, same era.
Differentially private mixture of generative neural networks
Gergely Acs, Luca Melis, Claude Castelluccia, and Emiliano De Cristofaro · 2018
Cited alongside, same era.
pmse mechanism: differentially private synthetic data with maximal distributional similarity
Joshua Snoke and Aleksandra Slavković · 2018
Cited alongside, same era.
Group normalization
Yuxin Wu and Kaiming He · 2018
Cited alongside, same era.
Tianshi Cao, Alex Bie, Arash Vahdat, Sanja Fidler, and Karsten Kreis · 2021
Later among the works it cites.
On the privacy risks of algorithmic fairness
Hongyan Chang and Reza Shokri · 2021
Later among the works it cites.
DP-MERF: Differentially private mean embeddings with random features for practical privacy-preserving data generation
Frederik Harder, Kamil Adamczewski, and Mijung Park · 2021
Later among the works it cites.
Differential privacy for census data, 2021
National Conference of State Legislatures · 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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Privsyn: Differentially private data synthesis
Zhikun Zhang, Tianhao Wang, Ninghui Li, Jean Honorio, Michael Backes, Shibo He, Jiming Chen, and Yang Zhang · 2021
Later among the works it cites.
Optimistic rates: A unifying theory for interpolation learning and regularization in linear regression, 2021
Lijia Zhou, Frederic Koehler, Danica J. Sutherland, and Nathan Srebro · 2021
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Differentially private diffusion models
Tim Dockhorn, Tianshi Cao, Arash Vahdat, and Karsten Kreis · 2022
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Disparate impact in differential privacy from gradient misalignment
Maria S Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo, and Jesse C Cresswell · 2022
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PEARL: Data synthesis via private embeddings and adversarial reconstruction learning
Seng Pei Liew, Tsubasa Takahashi, and Michihiko Ueno · 2022
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Differential privacy has bounded impact on fairness in classification
Paul Mangold, Michaël Perrot, Aurélien Bellet, and Marc Tommasi · 2022
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How unfair is private learning?
Amartya Sanyal, Yaxi Hu, and Fanny Yang · 2022
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Considerations for differentially private learning with large-scale public pretraining
Florian Tramèr, Gautam Kamath, and Nicholas Carlini · 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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Understanding disparate effects of membership inference attacks and their countermeasures
Da Zhong, Haipei Sun, Jun Xu, Neil Gong, and Wendy Hui Wang · 2022
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Alex Bie, Gautam Kamath, and Guojun Zhang · 2023
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