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Differentially Private-SGD (DP-SGD) of Abadi et al.
Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H. Brendan McMahan · 1905
Earlier work this paper cites.
Neural network studies, 1. comparison of overfitting and overtraining
Igor V. Tetko, David J. Livingstone, and Alexander I. Luik · 1995
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Understanding gradient clipping in private SGD: A geometric perspective
Xiangyi Chen, Zhiwei Steven Wu, and Mingyi Hong · 2006
Earlier work this paper cites.
Bypassing the ambient dimension: Private SGD with gradient subspace identification
Yingxue Zhou, Zhiwei Steven Wu, and Arindam Banerjee · 2007
Earlier work this paper cites.
Privacy amplification via random check-ins
Borja Balle, Peter Kairouz, H. Brendan McMahan, Om Thakkar, and Abhradeep Thakurta · 2007
Earlier work this paper cites.
Efficient per-example gradient computations
Ian Goodfellow · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 2017
Cited alongside, same era.
Automatic differentiation in machine learning: a survey
Atılım Günes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2017
Cited alongside, same era.
A new trick for calculating Jacobian vector products
Jamie Townsend · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Cited alongside, same era.
Gaussian differential privacy, 2019
Jinshuo Dong, Aaron Roth, and Weijie J. Su · 2019
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
Later among the works it cites.
Deep learning with gaussian differential privacy, 2019
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J. Su · 2019
Later among the works it cites.
Efficient per-example gradient computations in convolutional neural networks
Gaspar Rochette, Andre Manoel, and Eric W Tramel · 2019
Later among the works it cites.
Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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Generative models for effective ML on private, decentralized datasets
Sean Augenstein, H. Brendan McMahan, Daniel Ramage, Swaroop Ramaswamy, Peter Kairouz, Mingqing Chen, Rajiv Mathews, and Blaise Agüera y Arcas · 2020
Later among the works it cites.
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Gmail smart compose: Real-time assisted writing
Mia Xu Chen, Benjamin N. Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy Sohn, and Yonghui Wu · 2019
Cited alongside, same era.
Diversifying reply suggestions using a matching-conditional variational autoencoder
Budhaditya Deb, Peter Bailey, and Milad Shokouhi · 2019
Cited alongside, same era.
github.com/tensorflow/privacy
Library Tensorflow Privacy
Cited in the paper.
github.com/facebookresearch/pytorch-dp
Library Opacus
Cited in the paper.
https://github.com/tensorflow/tensorflow/issues/4897#issuecomment-290997283
Ian Goodfellow
Cited in the paper.
Fast per example gradient support
TFissue86
Cited in the paper.
Pranav Subramani, Nicholas Vadivelu, and Gautam Kamath · 2020
Later among the works it cites.
Scaling up differentially private deep learning with fast per-example gradient clipping
Jaewoo Lee and Daniel Kifer · 2021
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