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Differentially private GANs have proven to be a promising approach for generating realistic synthetic data without compromising the privacy of individuals.
Exponentiated gradient versus gradient descent for linear predictors
Jyrki Kivinen and Manfred K. Warmuth · 1996
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A decision-theoretic generalization of on-line learning and an application to boosting
Yoav Freund and Robert E Schapire · 1997
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Random forests
Leo Breiman · 2001
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DP-CGAN: differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2001
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N. Rothblum · 2010
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The multiplicative weights update method: a meta-algorithm and applications
Sanjeev Arora, Elad Hazan, and Satyen Kale · 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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Missforest—non-parametric missing value imputation for mixed-type data
Daniel J Stekhoven and Peter Bühlmann · 2012
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A stability-based validation procedure for differentially private machine learning
Kamalika Chaudhuri and Staal A Vinterbo · 2013
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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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
Cited alongside, same era.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
Cited alongside, same era.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Cited alongside, same era.
MGAN: Training generative adversarial nets with multiple generators
Quan Hoang, Tu Dinh Nguyen, Trung Le, and Dinh Phung · 2018
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Scalable private learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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General and specific utility measures for synthetic data
Joshua Snoke, Gillian M. Raab, Beata Nowok, Chris Dibben, and Aleksandra Slavkovic · 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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Discriminator rejection sampling
Samaneh Azadi, Catherine Olsson, Trevor Darrell, Ian J. Goodfellow, and Augustus Odena · 2019
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Privacy-preserving generative deep neural networks support clinical data sharing
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Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Cited alongside, same era.
Generalization and equilibrium in generative adversarial nets (GANs)
Sanjeev Arora, Rong Ge, Yingyu Liang, Tengyu Ma, and Yi Zhang · 2017
Cited alongside, same era.
Learning with privacy at scale
Differential Privacy Team, Apple · 2017
Cited alongside, same era.
Collecting telemetry data privately
Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Veegan: Reducing mode collapse in gans using implicit variational learning
Akash Srivastava, Lazar Valkov, Chris Russell, Michael U Gutmann, and Charles Sutton · 2017
Cited alongside, same era.
Adagan: Boosting generative models
Ilya Tolstikhin, Sylvain Gelly, Olivier Bousquet, Carl-Johann Simon-Gabriel, and Bernhard Schölkopf · 2017
Cited alongside, same era.
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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Differentially private generative adversarial networks for time series, continuous, and discrete open data
Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger · 2019
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Private selection from private candidates
Jingcheng Liu and Kunal Talwar · 2019
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Ipums usa: Version 9.0 [dataset]
Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas, and Matthew Sobek · 2019
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Metropolis-Hastings generative adversarial networks
Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, and Jason Yosinski · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2019
Later among the works it cites.
Christian Arnold and Marcel Neunhoeffer · 2020
Closest in time.
Bridging the gap between f f -gans and wasserstein gans, 2020
Jiaming Song and Stefano Ermon · 2020
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