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In this technical report, we explore the use of homomorphic encryption (HE) in the context of training and predicting with deep learning (DL) models to deliver strict \textit{Privacy by Design} services, and to enforce a zero-trust model of data governance.
How to share a secret
Adi Shamir · 1979
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
Earlier work this paper cites.
Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
Earlier work this paper cites.
Sharing decryption in the context of voting or lotteries
Pierre-Alain Fouque, Guillaume Poupard, and Jacques Stern · 2000
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Fully homomorphic encryption using ideal lattices
Craig Gentry · 2009
Earlier work this paper cites.
The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans
Samuel G Armato III, Geoffrey McLennan, Luc Bidaut, Michael F McNitt-Gray, Charles R Meyer, Anthony P Reeves, Binsheng Zhao, Denise R Aberle, Claudia I Henschke, Eric A Hoffman, et al · 2011
Earlier work this paper cites.
(leveled) fully homomorphic encryption without bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan · 2012
Earlier work this paper cites.
Private predictive analysis on encrypted medical data
Joppe W Bos, Kristin Lauter, and Michael Naehrig · 2014
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Algorithms in helib
Shai Halevi and Victor Shoup · 2014
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A guide to fully homomorphic encryption
Frederik Armknecht, Colin Boyd, Christopher Carr, Kristian Gjøsteen, Angela Jäschke, Christian A Reuter, and Martin Strand · 2015
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A review of homomorphic encryption and software tools for encrypted statistical machine learning
Louis JM Aslett, Pedro M Esperança, and Chris C Holmes · 2015
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Large dataset of labeled optical coherence tomography (oct) and chest x-ray images
Daniel Kermany, Kang Zhang, and Michael Goldbaum · 2018
Later among the works it cites.
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
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Low latency privacy preserving inference
Alon Brutzkus, Ran Gilad-Bachrach, and Oren Elisha · 2019
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A hybrid approach to privacy-preserving federated learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar, Thomas Steinke, Heiko Ludwig, Rui Zhang, and Yi Zhou · 2019
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Octid: Optical coherence tomography image database
Peyman Gholami, Priyanka Roy, Mohana Kuppuswamy Parthasarathy, and Vasudevan Lakshminarayanan · 2020
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Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
Cited alongside, same era.
Homomorphic encryption for arithmetic of approximate numbers
Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo Song · 2017
Cited alongside, same era.
Oblivious neural network predictions via minionn transformations
Jian Liu, Mika Juuti, Yao Lu, and Nadarajah Asokan · 2017
Cited alongside, same era.
{ \{ GAZELLE } \} : A low latency framework for secure neural network inference
Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan
Cited in the paper.
{ \{ XONN } \} : Xnor-based oblivious deep neural network inference
M Sadegh Riazi, Mohammad Samragh, Hao Chen, Kim Laine, Kristin Lauter, and Farinaz Koushanfar
Cited in the paper.
Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, and Yang Liu · 2020
Later among the works it cites.
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, Andreas Saleh, Marcus Makowski, Daniel Rueckert, and Rickmer Braren · 2021
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