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Advancements in deep learning enable cloud servers to provide inference-as-a-service for clients.
How to generate and exchange secrets
Andrew Yao · 1986
Earlier work this paper cites.
How to play any mental game
Oded Goldreich, Silvio Micali, and Avi Wigderson · 1987
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Correlated pseudorandomness and the complexity of private computations
Donald Beaver · 1996
Earlier work this paper cites.
Public-key cryptosystems based on composite degree residuosity classes
Pascal Paillier · 1999
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Privacy preserving auctions and mechanism design
Moni Naor, Benny Pinkas, and Reuban Sumner · 1999
Earlier work this paper cites.
Extending oblivious transfers efficiently
Yuval Ishai, Joe Kilian, Kobbi Nissim, and Erez Petrank · 2003
Earlier work this paper cites.
How to exchange secrets with oblivious transfer
Michael O Rabin · 2005
Earlier work this paper cites.
A privacy-preserving protocol for neural-network-based computation
Mauro Barni, Claudio Orlandi, and Alessandro Piva · 2006
Earlier work this paper cites.
Oblivious neural network computing via homomorphic encryption
Claudio Orlandi, Alessandro Piva, and Mauro Barni · 2007
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Improved garbled circuit: Free XOR gates and applications
Vladimir Kolesnikov and Thomas Schneider · 2008
Earlier work this paper cites.
Generalized universal circuits for secure evaluation of private functions with application to data classification
Ahmad-Reza Sadeghi and Thomas Schneider · 2008
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A proof of security of Yao’s protocol for two-party computation
Yehuda Lindell and Benny Pinkas · 2009
Earlier work this paper cites.
Privacy-preserving ECG classification with branching programs and neural networks
Mauro Barni, Pierluigi Failla, Riccardo Lazzeretti, Ahmad-Reza Sadeghi, and Thomas Schneider · 2011
Earlier work this paper cites.
Secure two-party computation via cut-and-choose oblivious transfer
Yehuda Lindell and Benny Pinkas · 2012
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Fully homomorphic encryption without modulus switching from classical gapsvp
Zvika Brakerski · 2012
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Somewhat practical fully homomorphic encryption
Junfeng Fan and Frederik Vercauteren · 2012
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More efficient oblivious transfer and extensions for faster secure computation
Gilad Asharov, Yehuda Lindell, Thomas Schneider, and Michael Zohner · 2013
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Efficient secure two-party computation using symmetric cut-and-choose
Yan Huang, Jonathan Katz, and David Evans · 2013
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Efficient garbling from a fixed-key blockcipher
Mihir Bellare, Viet Tung Hoang, Sriram Keelveedhi, and Phillip Rogaway · 2013
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Efficient fully homomorphic encryption from (standard) lwe
Zvika Brakerski and Vinod Vaikuntanathan · 2014
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(leveled) fully homomorphic encryption without bootstrapping
Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan · 2014
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Fitnets: Hints for thin deep nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, et al · 2015
Cited alongside, same era.
Learning both weights and connections for efficient neural network
Song Han, Jeff Pool, John Tran, and William Dally · 2015
Cited alongside, same era.
Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 2015
Cited alongside, same era.
Two halves make a whole
Samee Zahur, Mike Rosulek, and David Evans · 2015
Cited alongside, same era.
TinyGarble: Highly compressed and scalable sequential garbled circuits
Deep models under the GAN: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
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Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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Chameleon: A hybrid secure computation framework for machine learning applications
M Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim M Songhori, Thomas Schneider, and Farinaz Koushanfar · 2018
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GAZELLE: A low latency framework for secure neural network inference
Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan · 2018
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Ebrahim M Songhori, Siam U Hussain, Ahmad-Reza Sadeghi, Thomas Schneider, and Farinaz Koushanfar · 2015
Cited alongside, same era.
ABY-a framework for efficient mixed-protocol secure two-party computation
Daniel Demmler, Thomas Schneider, and Michael Zohner · 2015
Cited alongside, same era.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
Predicting the sequence specificities of dna-and rna-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
Cited alongside, same era.
Stealing machine learning models via prediction APIs
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
Cited alongside, same era.
CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy
Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing · 2016
Cited alongside, same era.
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 2016
Cited alongside, same era.
DeepSecure: Scalable provably-secure deep learning
Bita Darvish Rouhani, M Sadegh Riazi, and Farinaz Koushanfar · 2018
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ReBNet: Residual binarized neural network
Mohammad Ghasemzadeh, Mohammad Samragh, and Farinaz Koushanfar · 2018
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Privacy-preserving machine learning as a service
Ehsan Hesamifard, Hassan Takabi, Mehdi Ghasemi, and Rebecca N Wright · 2018
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Faster CryptoNets: Leveraging sparsity for real-world encrypted inference
Edward Chou, Josh Beal, Daniel Levy, Serena Yeung, Albert Haque, and Li Fei-Fei · 2018
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HyCC: Compilation of hybrid protocols for practical secure computation
Niklas Büscher, Daniel Demmler, Stefan Katzenbeisser, David Kretzmer, and Thomas Schneider · 2018
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Privacy-preserving deep learning and inference
M Sadegh Riazi and Farinaz Koushanfar · 2018
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ABY3: a mixed protocol framework for machine learning
Payman Mohassel and Peter Rindal · 2018
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SecureNN: Efficient and private neural network training, 2018
Sameer Wagh, Divya Gupta, and Nishanth Chandran · 2018
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Secure outsourced matrix computation and application to neural networks
Xiaoqian Jiang, Miran Kim, Kristin Lauter, and Yongsoo Song · 2018
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TAPAS: Tricks to accelerate (encrypted) prediction as a service
Amartya Sanyal, Matt Kusner, Adria Gascon, and Varun Kanade · 2018
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Michaela Hardt, Peter J Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, et al · 2018
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Deep learning on private data
M Sadegh Riazi, Bita Darvish Rouhani, and Farinaz Koushanfar · 2019
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A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar, Volodymyr Kuleshov, Mark DePristo, Katherine Chou, Claire Cui, Greg Corrado, Sebastian Thrun, and Jeff Dean · 2019
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https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
Breast Cancer Wisconsin, accessed on 01/20/2019 · 2019
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https://www.kaggle.com/uciml/pima-indians-diabetes-database
Pima Indians Diabetes, accessed on 01/20/2019 · 2019
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https://www.kaggle.com/uciml/indian-liver-patient-records
Indian Liver Patient Records, accessed on 01/20/2019 · 2019
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https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria
Malaria Cell Images, accessed on 01/20/2019 · 2019
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