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The use of collaborative and decentralized machine learning techniques such as federated learning have the potential to enable the development and deployment of clinical risk predictions models in low-resource settings without requiring sensitive data be shared or stored in a central repository.
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach
E R DeLong, D M DeLong, and D L Clarke-Pearson · 1988
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Committee on ensuring the utility and integrity of research data in a digital age
Daniel Kleppner and P Sharp · 2009
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A systematic review of re-identification attacks on health data
Khaled El Emam, Elizabeth Jonker, Luk Arbuckle, and Bradley Malin · 2011
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A stability-based validation procedure for differentially private machine learning
Kamalika Chaudhuri and Staal A Vinterbo · 2013
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Publishing data from electronic health records while preserving privacy: A survey of algorithms
Aris Gkoulalas-Divanis, Grigorios Loukides, and Jimeng Sun · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, and Others · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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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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Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Rényi differential privacy
I. Mironov · 2017
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The eicu collaborative research database, a freely available multi-center database for critical care research
Privacy-Preserving Distributed Deep Learning for Clinical Data
Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, and Zhiwei Steven Wu · 2018
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A General Approach to Adding Differential Privacy to Iterative Training Procedures
H. Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz · 2018
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Distributed deep learning networks among institutions for medical imaging
Ken Chang, Niranjan Balachandar, Carson Lam, Darvin Yi, James Brown, Andrew Beers, Bruce Rosen, Daniel L Rubin, and Jayashree Kalpathy-Cramer · 2018
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Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
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Tom J Pollard, Alistair EW Johnson, Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi · 2018
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The Secret Sharer: Measuring Unintended Neural Network Memorization & Extracting Secrets
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song · 2018
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No Peek: A Survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar, Otkrist Gupta, and Abhimanyu Dubey
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Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas
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Learning Differentially Private Recurrent Language Models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar
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Reducing leakage in distributed deep learning for sensitive health data
Praneeth Vepakomma, Otkrist Gupta, Abhimanyu Dubey, and Ramesh Raskar
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Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Private Selection from Private Candidates
Jingcheng Liu and Kunal Talwar · 2018
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Privacy-preserving generative deep neural networks support clinical data sharing
Brett K Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, Ran Lee, Sanjeev P Bhavnani, James Brian Byrd, and Casey S Greene · 2019
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