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The vanilla Differentially-Private Stochastic Gradient Descent (DP-SGD), including DP-Adam and other variants, ensures the privacy of training data by uniformly distributing privacy costs across training steps.
Gradient-based learning applied to document recognition
Y. Lecun and L. Bottou · 1998
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Differential privacy
Cynthia Dwork · 2006
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Deep learning with differential privacy
Martín Abadi, Andy Chu, Ian J. Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Efficient private erm for smooth objectives
Jiaqi Zhang, Kai Zheng, Wenlong Mou, and Liwei Wang · 2017
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Optimization methods for large-scale machine learning
Léon Bottou, Frank E Curtis, and Jorge Nocedal · 2018
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Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al · 2018
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Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
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Adaclip: Adaptive clipping for private sgd
Venkatadheeraj Pichapati, Ananda Theertha Suresh, Felix X. Yu, Sashank J. Reddi, and Sanjiv Kumar · 2019
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Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H. Brendan McMahan · 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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Differentially private model publishing for deep learning
Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, and Stacey Truex · 2019
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Privacy profiles and amplification by subsampling
Borja Balle, Gilles Barthe, and Marco Gaboardi · 2020
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Deep learning with Gaussian differential privacy
Zhiqi Bu, Jinshuo Dong, Qi Long, and Weijie J. Su · 2020
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Understanding gradient clipping in private SGD: A geometric perspective
Deep leakage from gradients
Ligeng Zhu and Song Han · 2020
Later among the works it cites.
Differentially private learning with adaptive clipping
Galen Andrew, Om Thakkar, H Brendan McMahan, and Swaroop Ramaswamy · 2021
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Numerical composition of differential privacy
Sivakanth Gopi, Yin Tat Lee, and Lukas Wutschitz · 2021
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Tighter generalization bounds for iterative privacy-preserving algorithms
Fengxiang He, Bohan Wang, and Dacheng Tao · 2021
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autodp: A flexible and easy-to-use package for differential privacy
Yuxiang Wang · 2021
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Xiangyi Chen, Zhiwei Steven Wu, and Mingyi Hong · 2020
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Characterizing private clipped gradient descent on convex generalized linear problems
Shuang Song, Om Thakkar, and Abhradeep Thakurta · 2020
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idlg: Improved deep leakage from gradients
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Private stochastic non-convex optimization: Adaptive algorithms and tighter generalization bounds
Yingxue Zhou, Xiangyi Chen, Mingyi Hong, Zhiwei Steven Wu, and Arindam Banerjee · 2020
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Adaptive privacy preserving deep learning algorithms for medical data
Xinyue Zhang, Jiahao Ding, Maoqiang Wu, Stephen TC Wong, Hien Van Nguyen, and Miao Pan
Cited in the paper.
Xiaoxia Wu, Lingxiao Wang, Irina Cristali, Quanquan Gu, and Rebecca Willett · 2021
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Adaptive privacy preserving deep learning algorithms for medical data
Xinyue Zhang, Jiahao Ding, Maoqiang Wu, Stephen T. C. Wong, Hien Van Nguyen, and Miao Pan · 2021
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Optimal accounting of differential privacy via characteristic function
Yuqing Zhu, Jinshuo Dong, and Yu-Xiang Wang · 2021
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Medical imaging deep learning with differential privacy
Alexander Ziller, Dmitrii Usynin, Rickmer Braren, Marcus Makowski, Daniel Rueckert, and Georgios Kaissis · 2021
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