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With the increased attention and legislation for data-privacy, collaborative machine learning (ML) algorithms are being developed to ensure the protection of private data used for processing.
The byzantine generals problem
Leslie Lamport, Robert Shostak, and Marshall Pease · 1982
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The sybil attack
John R. Douceur · 2002
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Privacy-preserving multivariate statistical analysis: Linear regression and classification
Wenliang Du, Yunghsiang Han, and Shigang Chen · 2004
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Advances and open problems in federated learning, 2019
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2004
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Differential privacy
Cynthia Dwork · 2006
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Learning in a large function space: Privacy-preserving mechanisms for SVM learning
Benjamin I. P. Rubinstein, Peter L. Bartlett, Ling Huang, and Nina Taft · 2009
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Differentially private empirical risk minimization, 2009
Kamalika Chaudhuri, Claire Monteleoni, and Anand D. Sarwate · 2009
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Privacy-preserving ecg classification with branching programs and neural networks
M. Barni, P. Failla, R. Lazzeretti, A. Sadeghi, and T. Schneider · 2011
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Poisoning attacks against support vector machines, 2012
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2012
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Functional mechanism: Regression analysis under differential privacy
Jun Zhang, Zhenjie Zhang, Xiaokui Xiao, Yin Yang, and Marianne Winslett · 2012
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Stochastic gradient descent with differentially private updates
S. Song, K. Chaudhuri, and A. D. Sarwate · 2013
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart · 2014
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Generative adversarial networks, 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Private predictive analysis on encrypted medical data
Joppe Bos, Kristin Lauter, and Michael Naehrig · 2014
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Privacy preserving back-propagation neural network learning made practical with cloud computing
J. Yuan and S. Yu · 2014
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Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation
Qi Li, Yaliang Li, Jing Gao, Bo Zhao, Wei Fan, and Jiawei Han · 2014
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SparkNet: Training Deep Networks in Spark
Philipp Moritz, Robert Nishihara, Ion Stoica, and Michael I. Jordan · 2015
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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Fast and secure three-party computation: The garbled circuit approach
Payman Mohassel, Mike Rosulek, and Ye Zhang · 2015
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Federated learning of deep networks using model averaging
H. Brendan McMahan, Eider Moore, Daniel Ramage, and Blaise Agüera y Arcas · 2016
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Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital – a real life proof of concept
Arthur Jochems, Timo M. Deist, Johan van Soest, Michael Eble, Paul Bulens, Philippe Coucke, Wim Dries, Philippe Lambin, and Andre Dekker · 2016
Earlier work this paper cites.
Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart · 2016
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Towards the science of security and privacy in machine learning
Nicolas Papernot, Patrick D. McDaniel, Arunesh Sinha, and Michael P. Wellman · 2016
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Microsoft’s ai twitter bot goes dark after racist, sexist tweets, 2016
Amy Tennery and Gina Cherelus · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
Jakub Konecný, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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.
High-throughput semi-honest secure three-party computation with an honest majority
Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara · 2016
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy, February 2016
Dowlin Nathan, Gilad-Bachrach Ran, Laine Kim, Lauter Kristin, Naehrig Michael, and Wernsing John · 2016
Cited alongside, same era.
Mitigating sybils in federated learning poisoning
Clement Fung, Chris J. M. Yoon, and Ivan Beschastnikh · 2018
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Reconstruction of training samples from loss functions
Akiyoshi Sannai · 2018
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Privacy-preserving deep learning via additively homomorphic encryption
L. T. Phong, Y. Aono, T. Hayashi, L. Wang, and S. Moriai · 2018
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Threat of adversarial attacks on deep learning in computer vision: A survey
Naveed Akhtar and Ajmal Mian · 2018
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Analyzing federated learning through an adversarial lens, 2018
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2018
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Privacy preserving deep computation model on cloud for big data feature learning
Q. Zhang, L. T. Yang, and Z. Chen · 2016
Cited alongside, same era.
Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: eurocat
Timo M. Deist, A. Jochems, Johan van Soest, Georgi Nalbantov, Cary Oberije, Seán Walsh, Michael Eble, Paul Bulens, Philippe Coucke, Wim Dries, Andre Dekker, and Philippe Lambin · 2017
Cited alongside, same era.
Developing and validating a survival prediction model for nsclc patients through distributed learning across 3 countries
Arthur Jochems, Timo M. Deist, Issam El Naqa, Marc Kessler, Chuck Mayo, Jackson Reeves, Shruti Jolly, Martha Matuszak, Randall Ten Haken, Johan van Soest, Cary Oberije, Corinne Faivre-Finn, Gareth Price, Dirk de Ruysscher, Philippe Lambin, and Andre Dekker · 2017
Cited alongside, same era.
Privacy-preserving deep learning: Revisited and enhanced
Le Trieu Phong, Yoshinori Aono, Takuya Hayashi, Lihua Wang, and Shiho Moriai · 2017
Cited alongside, same era.
Deep models under the GAN: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
Cited alongside, same era.
Targeted backdoor attacks on deep learning systems using data poisoning
Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Cited alongside, same era.
Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter L. Bartlett · 2018
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Exploiting unintended feature leakage in collaborative learning
Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov · 2018
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Blind justice: Fairness with encrypted sensitive attributes
Niki Kilbertus, Adria Gascon, Matt Kusner, Michael Veale, Krishna Gummadi, and Adrian Weller · 2018
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Privpy: Enabling scalable and general privacy-preserving computation
Yi Li, Yitao Duan, and Wei Xu · 2018
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Aby3: A mixed protocol framework for machine learning
Payman Mohassel and Peter Rindal · 2018
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The hidden vulnerability of distributed learning in byzantium, 2018
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault · 2018
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Federated learning for emoji prediction in a mobile keyboard
Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays · 2019
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Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records
Li Huang, Andrew L. Shea, Huining Qian, Aditya Masurkar, Hao Deng, and Dianbo Liu · 2019
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Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation
Micah J. Sheller, G. Anthony Reina, Brandon Edwards, Jason Martin, and Spyridon Bakas · 2019
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Deep leakage from gradients, 2019
Ligeng Zhu, Zhijian Liu, and Song Han · 2019
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Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer, 2019
Hongyan Chang, Virat Shejwalkar, Reza Shokri, and Amir Houmansadr · 2019
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Neural network model extraction attacks in edge devices by hearing architectural hints
Xing Hu, Ling Liang, Lei Deng, Shuangchen Li, Xinfeng Xie, Yu Ji, Yufei Ding, Chang Liu, Timothy Sherwood, and Yuan Xie · 2019
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An end-to-end encrypted neural network for gradient updates transmission in federated learning
Hongyu Li and Tianqi Han · 2019
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Federated learning with bayesian differential privacy, 2019
Aleksei Triastcyn and Boi Faltings · 2019
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Robust aggregation for federated learning, 12 2019
Krishna Pillutla, Sham Kakade, and Zaid Harchaoui · 2019
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Measuring the effects of non-identical data distribution for federated visual classification, 2019
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2019
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Federated learning: Challenges, methods, and future directions, 2019
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2019
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Hybridalpha
Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar, and Heiko Ludwig · 2019
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Threats to federated learning: A survey, 2020
Lingjuan Lyu, Han Yu, and Qiang Yang · 2020
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idlg: Improved deep leakage from gradients, 2020
Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen · 2020
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Federated continual learning with adaptive parameter communication, 2020
Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, and Sung Ju Hwang · 2020
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