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Train machine learning models on sensitive user data has raised increasing privacy concerns in many areas.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Gradient-based learning applied to document recognition
Yann LeCun, L e ´ \acute{e} on Bottou, Yoshua Bengio, and Geoffrey Hinton · 1998
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Tor: The second-generation onionrouter
Roger Dingledine, Nick Mathewson, and Paul Syverson · 2004
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Differential privacy
Cynthia Dwork · 2011
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Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Rappor: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
Cited alongside, same era.
Collecting and analyzing data from smart device users with local differential privacy
Thông T Nguyên, Xiaokui Xiao, Yin Yang, Siu Cheung Hui, Hyejin Shin, and Junbum Shin · 2016
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
Cited alongside, same era.
Differentially private federated learning: A client level perspective
The privacy blanket of the shuffle model
Borja Balle, James Bell, Adria Gascón, and Kobbi Nissim · 2019
Later among the works it cites.
Distributed differential privacy via shuffling
Albert Cheu, Adam Smith, Jonathan Ullman, David Zeber, and Maxim Zhilyaev · 2019
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Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
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How to change your ip address
Rosette Belesi · 2020
Closest in time.
Privacy and robustness in federated learning: Attacks and defenses
Lingjuan Lyu, Han Yu, Xingjun Ma, Lichao Sun, Jun Zhao, Qiang Yang, and Philip S Yu · 2020
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Robin C Geyer, Tassilo Klein, and Moin Nabi · 2017
Cited alongside, same era.
Learning differentially private recurrent language models
H Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Cited alongside, same era.
Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martin Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
Cited alongside, same era.
Minimax optimal procedures for locally private estimation
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2018
Cited alongside, same era.
Mohamed Seif, Ravi Tandon, and Ming Li · 2020
Closest in time.
Federated model distillation with noise-free differential privacy
Lichao Sun and Lingjuan Lyu · 2020
Closest in time.
Ldp-fed: federated learning with local differential privacy
Stacey Truex, Ling Liu, Ka-Ho Chow, Mehmet Emre Gursoy, and Wenqi Wei · 2020
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Federated multi-view learning for private medical data integration and analysis
Sicong Che, Hao Peng, Lichao Sun, Yong Chen, and Lifang He · 2021
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Fedmood: Federated learning on mobile health data for mood detection
Xiaohang Xu, Hao Peng, Lichao Sun, Md Zakirul Alam Bhuiyan, Lianzhong Liu, and Lifang He · 2021
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