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We propose AriaNN, a low-interaction privacy-preserving framework for private neural network training and inference on sensitive data.
How to generate and exchange secrets
Andrew Chi-Chih Yao · 1986
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Efficient multiparty protocols using circuit randomization
Donald Beaver · 1991
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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High performance convolutional neural networks for document processing
Kumar Chellapilla, Sidd Puri, and Patrice Simard · 2006
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Sharemind: A framework for fast privacy-preserving computations
Dan Bogdanov, Sven Laur, and Jan Willemson · 2008
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Foundations of Cryptography: volume 2, Basic Applications
Oded Goldreich · 2009
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MNIST handwritten digit database
Yann LeCun, Corinna Cortes, and C. J. Burges · 2010
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Multiparty computation from somewhat homomorphic encryption
Ivan Damgård, Valerio Pastro, Nigel Smart, and Sarah Zakarias · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2012
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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The CIFAR-10 dataset
Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Function secret sharing
Elette Boyle, Niv Gilboa, and Yuval Ishai · 2015
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Aby-a framework for efficient mixed-protocol secure two-party computation
Daniel Demmler, Thomas Schneider, and Michael Zohner · 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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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, and Michael Bernstein · 2015
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Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
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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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High-throughput semi-honest secure three-party computation with an honest majority
Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara · 2016
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Optimizing semi-honest secure multiparty computation for the internet
Aner Ben-Efraim, Yehuda Lindell, and Eran Omri · 2016
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Function secret sharing: Improvements and extensions
Elette Boyle, Niv Gilboa, and Yuval Ishai · 2016
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Faster fully homomorphic encryption: Bootstrapping in less than 0.1 seconds
Ilaria Chillotti, Nicolas Gama, Mariya Georgieva, and Malika Izabachene · 2016
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A survey of secure multiparty computation protocols for privacy preserving genetic tests
Tamara Dugan and Xukai Zou · 2016
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Mascot: faster malicious arithmetic secure computation with oblivious transfer
Marcel Keller, Emmanuela Orsini, and Peter Scholl · 2016
Cited alongside, same era.
Federated learning: Strategies for improving communication efficiency
A generic framework for privacy preserving deep learning
Théo Ryffel, Andrew Trask, Morten Dahl, Bobby Wagner, Jason Mancuso, Daniel Rueckert, and Jonathan Passerat-Palmbach · 2018
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http://sealcrypto.org , October 2018
Microsoft SEAL (release 3.0) · 2018
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Securenn: Efficient and private neural network training
Sameer Wagh, Divya Gupta, and Nishanth Chandran · 2018
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Quotient: two-party secure neural network training and prediction
Nitin Agrawal, Ali Shahin Shamsabadi, Matt J Kusner, and Adrià Gascón · 2019
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Privacy-preserving machine learning: Threats and solutions
Mohammad Al-Rubaie and J. Morris Chang · 2019
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nGraph-HE2: A high-throughput framework for neural network inference on encrypted data
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Jakub Konečnỳ, H Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
Cited alongside, same era.
The cut-and-choose game and its application to cryptographic protocols
Ruiyu Zhu, Yan Huang, Jonathan Katz, and Abhi Shelat · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Deep models under the gan: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Perez-Cruz · 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.
Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
Cited alongside, same era.
Fabian Boemer, Anamaria Costache, Rosario Cammarota, and Casimir Wierzynski · 2019
Later among the works it cites.
nGraph-HE: a graph compiler for deep learning on homomorphically encrypted data
Fabian Boemer, Yixing Lao, Rosario Cammarota, and Casimir Wierzynski · 2019
Later among the works it cites.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konecny, Stefano Mazzocchi, and H. Brendan McMahan · 2019
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Secure computation with preprocessing via function secret sharing
Elette Boyle, Niv Gilboa, and Yuval Ishai · 2019
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Privacy-preserving contextual bandits
Awni Hannun, Brian Knott, Shubho Sengupta, and Laurens van der Maaten · 2019
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How to backdoor federated learning
Eugene Bagdasaryan, Andreas Veit, Yiqing Hua, Deborah Estrin, and Vitaly Shmatikov · 2020
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Function secret sharing for mixed-mode and fixed-point secure computation
Elette Boyle, Nishanth Chandran, Niv Gilboa, Divya Gupta, Yuval Ishai, Nishant Kumar, and Mayank Rathee · 2020
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Flash: fast and robust framework for privacy-preserving machine learning
Megha Byali, Harsh Chaudhari, Arpita Patra, and Ajith Suresh · 2020
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Trident: Efficient 4pc framework for privacy preserving machine learning
Harsh Chaudhari, Rahul Rachuri, and Ajith Suresh · 2020
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Cryptflow: Secure tensorflow inference
Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, and Rahul Sharma · 2020
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Blaze: Blazing fast privacy-preserving machine learning
Arpita Patra and Ajith Suresh · 2020
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Privacy-preserving contact tracing of covid-19 patients
Leonie Reichert, Samuel Brack, and Björn Scheuermann · 2020
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Falcon: Honest-majority maliciously secure framework for private deep learning
Sameer Wagh, Shruti Tople, Fabrice Benhamouda, Eyal Kushilevitz, Prateek Mittal, and Tal Rabin · 2020
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The secret revealer: generative model-inversion attacks against deep neural networks
Yuheng Zhang, Ruoxi Jia, Hengzhi Pei, Wenxiao Wang, Bo Li, and Dawn Song · 2020
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Fantastic four: Honest-majority four-party secure computation with malicious security
Anders Dalskov, Daniel Escudero, and Marcel Keller · 2021
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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, et al · 2021
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