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Standard methods for differentially private training of deep neural networks replace back-propagated mini-batch gradients with biased and noisy approximations to the gradient.
Learning representations by back-propagating errors
D. E. Rumelhart, G. E. Hinton, and R. J. Williams · 1986
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Competitive learning: From interactive activation to adaptive resonance
S. Grossberg · 1987
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Our data, ourselves: Privacy via distributed noise generation
C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor · 2006
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2014
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Difference target propagation
D.-H. Lee, S. Zhang, A. Fischer, and Y. Bengio · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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How important is weight symmetry in backpropagation?
Q. Liao, J. Z. Leibo, and T. Poggio · 2016
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Random synaptic feedback weights support error backpropagation for deep learning
T. P. Lillicrap, D. Cownden, D. B. Tweed, and C. J. Akerman · 2016
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Direct feedback alignment provides learning in deep neural networks
A. Nøkland · 2016
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Differential privacy preservation for deep auto-encoders: an application of human behavior prediction
N. Phan, Y. Wang, X. Wu, and D. Dou · 2016
Cited alongside, same era.
Differentially private mixture of generative neural networks
G. Acs, L. Melis, C. Castelluccia, and E. D. Cristofaro · 2017
Cited alongside, same era.
Wasserstein generative adversarial networks
M. Arjovsky, S. Chintala, and L. Bottou · 2017
Cited alongside, same era.
Renyi differential privacy
I. Mironov · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
N. Papernot, M. Abadi, Úlfar Erlingsson, I. Goodfellow, and K. Talwar · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
Scalable private learning with pate
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and Úlfar Erlingsson · 2018
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Differentially private generative adversarial network, 2018
L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou · 2018
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Deep learning without weight transport
M. Akrout, C. Wilson, P. Humphreys, T. Lillicrap, and D. B. Tweed · 2019
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Bounding user contributions: A bias-variance trade-off in differential privacy
K. Amin, A. Kulesza, A. Munoz, and S. Vassilvtiskii · 2019
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Differential privacy has disparate impact on model accuracy
E. Bagdasaryan, O. Poursaeed, and V. Shmatikov · 2019
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Direct feedback alignment with sparse connections for local learning
B. Crafton, A. Parihar, E. Gebhardt, and A. Raychowdhury · 2019
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Privacy preserving synthetic data release using deep learning
N. C. Abay, Y. Zhou, M. Kantarcioglu, B. M. Thuraisingham, and L. Sweeney · 2018
Cited alongside, same era.
Assessing the scalability of biologically-motivated deep learning algorithms and architectures
S. Bartunov, A. Santoro, B. Richards, L. Marris, G. E. Hinton, and T. Lillicrap · 2018
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Privacy-preserving generative deep neural networks support clinical data sharing
B. K. Beaulieu-Jones, Z. S. Wu, C. Williams, R. Lee, S. P. Bhavnani, J. B. Byrd, and C. S. Greene · 2018
Cited alongside, same era.
Differentially private data generative models
Q. Chen, C. Xiang, M. Xue, B. Li, N. Borisov, D. Kaafar, and H. Zhu · 2018
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
H. B. McMahan, G. Andrew, U. Erlingsson, S. Chien, I. Mironov, N. Papernot, and P. Kairouz · 2018
Cited alongside, same era.
Learning differentially private recurrent language models
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2018
Cited alongside, same era.
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Direct feedback alignment based convolutional neural network training for low-power online learning processor
D. Han and H.-j. Yoo · 2019
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Differentially private learning with adaptive clipping
O. Thakkar, G. Andrew, and H. B. McMahan · 2019
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Subsampled renyi differential privacy and analytical moments accountant
Y.-X. Wang, B. Balle, and S. P. Kasiviswanathan · 2019
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Differentially private model publishing for deep learning
L. Yu, L. Liu, C. Pu, M. E. Gursoy, and S. Truex · 2019
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Poission subsampled rényi differential privacy
Y. Zhu and Y.-X. Wang · 2019
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Removing disparate impact of differentially private stochastic gradient descent on model accuracy, 2020
D. Xu, W. Du, and X. Wu · 2020
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