Understand
Deep learning with medical data often requires larger samples sizes than are available at single providers.
- While data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data.
- This problem has motivated a number of studies on distributed training of neural networks that do not require direct sharing of the training data.
- However, simple distributed training does not offer provable privacy guarantees to satisfy technical safe standards and may reveal information about the underlying patients.
Built on
Differentiation of lobular versus ductal breast carcinomas by expression microarray analysis
James E Korkola, Sandy DeVries, Jane Fridlyand, Shelley E. Hwang, Anne L. H. Estep, Yunn-Yi Chen, Karen L. Chew, Shanaz H. Dairkee, Ronald M. Jensen, and Frederic M. Waldman · 2003
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
Earlier work this paper cites.
Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V Pearson, Dietrich A Stephan, Stanley F Nelson, and David W Craig · 2008
Earlier work this paper cites.
Robust de-anonymization of large sparse datasets
Arvind Narayanan and Vitaly Shmatikov · 2008
Earlier work this paper cites.
The cancer genome atlas pan-cancer analysis project
John N. Weinstein, Eric A. Collisson, Gordon B. Mills, Kenna M. Shaw, Brad A. Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sanders, Joshua M. Stuart, and Cancer Genome Atlas Research Network · 2010
Earlier work this paper cites.
A systematic review of re-identification attacks on health data
Khaled El Emam, Elizabeth Jonker, Luk Arbuckle, and Bradley Malin · 2011
Earlier work this paper cites.
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 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.
Semi-supervised learning of the electronic health record for phenotype stratification
Brett K Beaulieu-Jones, Casey S Greene, et al · 2016
Cited alongside, same era.
Privacy-preserving generative deep neural networks support clinical data sharing
Brett K Beaulieu-Jones, Zhiwei Steven Wu, Chris Williams, and Casey S Greene · 2017
Cited alongside, same era.
Then
Rényi differential privacy
Ilya Mironov · 2017
Later among the works it cites.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Later among the works it cites.
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
Closest in time.
Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman, et al · 2018
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The eicu collaborative research database, a freely available multi-center database for critical care research
Tom J Pollard, Alistair EW Johnson, Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi · 2018
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