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Differentially private stochastic gradient descent (DPSGD) is a variation of stochastic gradient descent based on the Differential Privacy (DP) paradigm, which can mitigate privacy threats that arise from the presence of sensitive information in training data.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, et al · 1998
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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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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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The algorithmic foundations of differential privacy
C. Dwork, A. Roth, et al · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Very deep convolutional networks for large-scale image recognition, 2014
K. Simonyan and A. Zisserman · 2014
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Weight uncertainty in neural networks
C. Blundell, J. Cornebise, K. Kavukcuoglu, and D. Wierstra · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Gradient estimation using stochastic computation graphs
J. Schulman, N. Heess, T. Weber, and P. Abbeel · 2015
Cited alongside, same era.
Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
Cited alongside, same era.
Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
Cited alongside, same era.
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
A survey on security and privacy issues in internet-of-things
Y. Yang, L. Wu, G. Yin, L. Li, and H. Zhao · 2017
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Differential privacy and applications
T. Zhu, G. Li, W. Zhou, and S. Y. Philip · 2017
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Deep learning for classical japanese literature
T. Clanuwat, M. Bober-Irizar, A. Kitamoto, A. Lamb, K. Yamamoto, and D. Ha · 2018
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Local differential privacy for deep learning
M. Chamikara, P. Bertok, I. Khalil, D. Liu, and S. Camtepe · 2019
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Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
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Cited alongside, same era.
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.
Self-normalizing neural networks
G. Klambauer, T. Unterthiner, A. Mayr, and S. Hochreiter · 2017
Cited alongside, same era.
Rényi differential privacy
I. Mironov · 2017
Cited alongside, same era.
Rényi differential privacy
I. Mironov · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
Cited alongside, same era.
Later among the works it cites.
Adaclip: Adaptive clipping for private sgd, 2019
V. Pichapati, A. T. Suresh, F. X. Yu, S. J. Reddi, and S. Kumar · 2019
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Improved differentially private analysis of variance
M. Swanberg, I. Globus-Harris, I. Griffith, A. Ritz, A. Groce, and A. Bray · 2019
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Fixup initialization: Residual learning without normalization
H. Zhang, Y. N. Dauphin, and T. Ma · 2019
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Making the shoe fit: Architectures, initializations, and tuning for learning with privacy, 2020
N. Papernot, S. Chien, S. Song, A. Thakurta, and U. Erlingsson · 2020
Closest in time.
Accessed: 2020-January
https://github.com/tensorflow/privacy · 2020
Closest in time.