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Privacy preserving machine learning algorithms are crucial for learning models over user data to protect sensitive information.
Some methods of speeding up the convergence of iteration methods
Boris T Polyak · 1964
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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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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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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When random sampling preserves privacy
Kamalika Chaudhuri and Nina Mishra · 2006
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Privacy, accuracy, and consistency too: a holistic solution to contingency table release
Boaz Barak, Kamalika Chaudhuri, Cynthia Dwork, Satyen Kale, Frank McSherry, and Kunal Talwar · 2007
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Mechanism design via differential privacy
Frank McSherry and Kunal Talwar · 2007
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The netflix challenge
Jordan Ellenberg · 2008
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2009
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Learning in a large function space: Privacy-preserving mechanisms for svm learning
Benjamin IP Rubinstein, Peter L Bartlett, Ling Huang, and Nina Taft · 2009
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Privacy integrated queries: an extensible platform for privacy-preserving data analysis
Frank D McSherry · 2009
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Differential privacy and robust statistics
Cynthia Dwork and Jing Lei · 2009
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Recurrent neural network based language model
Tomáš Mikolov, Martin Karafiát, Lukáš Burget, Jan Černockỳ, and Sanjeev Khudanpur · 2010
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Boosting and differential privacy
Cynthia Dwork, Guy N Rothblum, and Salil Vadhan · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Cited alongside, same era.
Functional mechanism: regression analysis under differential privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett · 2012
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Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
Cited alongside, same era.
A stability-based validation procedure for differentially private machine learning
Kamalika Chaudhuri and Staal A Vinterbo · 2013
Cited alongside, same era.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Stochastic variance reduction for nonconvex optimization
Sashank J Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, and Alex Smola · 2016
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Lattice rescoring strategies for long short term memory language models in speech recognition
Shankar Kumar, Michael Nirschl, Daniel Holtmann-Rice, Hank Liao, Ananda Theertha Suresh, and Felix Yu · 2017
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Bolt-on differential privacy for scalable stochastic gradient descent-based analytics
Xi Wu, Fengan Li, Arun Kumar, Kamalika Chaudhuri, Somesh Jha, and Jeffrey Naughton · 2017
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Learning differentially private language models without losing accuracy
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Cited alongside, same era.
Differentially private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Cited alongside, same era.
Grammar as a foreign language
Oriol Vinyals, Łukasz Kaiser, Terry Koo, Slav Petrov, Ilya Sutskever, and Geoffrey Hinton · 2015
Cited alongside, same era.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
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.
The composition theorem for differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2017
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Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
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Optimal noise-adding mechanism in additive differential privacy
Quan Geng, Wei Ding, Ruiqi Guo, and Sanjiv Kumar · 2018
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cpSGD: Communication-efficient and differentially-private distributed SGD
Naman Agarwal, Ananda Theertha Suresh, Felix X. Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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Differentially private releasing via deep generative model (technical report)
Xinyang Zhang, Shouling Ji, and Ting Wang · 2018
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Three tools for practical differential privacy
Koen Lennart van der Veen, Ruben Seggers, Peter Bloem, and Giorgio Patrini · 2018
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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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P3sgd: Patient privacy preserving sgd for regularizing deep cnns in pathological image classification
Bingzhe Wu, Shiwan Zhao, Guangyu Sun, Xiaolu Zhang, Zhong Su, Caihong Zeng, and Zhihong Liu · 2019
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Differentially private learning with adaptive clipping
Om Thakkar, Galen Andrew, and H Brendan McMahan · 2019
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