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How to train a machine learning model while keeping the data private and secure? We present CodedPrivateML, a fast and scalable approach to this critical problem.
How to share a secret
Adi Shamir · 1979
Earlier work this paper cites.
Protocols for secure computations
Andrew C Yao · 1982
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Completeness theorems for non-cryptographic fault-tolerant distributed computation
Michael Ben-Or, Shafi Goldwasser, and Avi Wigderson · 1988
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Privacy preserving data mining
Yehuda Lindell and Benny Pinkas · 2000
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Weierstrass and approximation theory
Allan Pinkus · 2000
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Result analysis of the nips 2003 feature selection challenge
Isabelle Guyon, Steve Gunn, Asa Ben-Hur, and Gideon Dror · 2005
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MPI for Python
Lisandro Dalcín, Rodrigo Paz, and Mario Storti · 2005
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Scalable and unconditionally secure multiparty computation
Ivan Damgård and Jesper Buus Nielsen · 2007
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Perfectly-secure MPC with linear communication complexity
Zuzana Beerliová-Trubíniová and Martin Hirt · 2008
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Efficient multi-party computation with information-theoretic security
Zuzana Beerliová-Trubíniová · 2008
Earlier work this paper cites.
Hey, you, get off of my cloud: exploring information leakage in third-party compute clouds
Thomas Ristenpart, Eran Tromer, Hovav Shacham, and Stefan Savage · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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A fully homomorphic encryption scheme
Craig Gentry and Dan Boneh · 2009
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Privacy-preserving logistic regression
Kamalika Chaudhuri and Claire Monteleoni · 2009
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Multiparty differential privacy via aggregation of locally trained classifiers
Manas Pathak, Shantanu Rane, and Bhiksha Raj · 2010
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Optimization: Insights and Applications
J. Brinkhuis and V. Tikhomirov · 2011
Earlier work this paper cites.
Cross-vm side channels and their use to extract private keys
Yinqian Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2012
Cited alongside, same era.
ML confidential: Machine learning on encrypted data
Thore Graepel, Kristin Lauter, and Michael Naehrig · 2012
Cited alongside, same era.
A differentially private stochastic gradient descent algorithm for multiparty classification
Arun Rajkumar and Shivani Agarwal · 2012
Cited alongside, same era.
Elements of Information Theory
Thomas M Cover and Joy A Thomas · 2012
Cited alongside, same era.
Privacy-preserving ridge regression on hundreds of millions of records
Valeria Nikolaenko, Udi Weinsberg, Stratis Ioannidis, Marc Joye, Dan Boneh, and Nina Taft · 2013
Cited alongside, same era.
Cross-tenant side-channel attacks in paas clouds
Yinqian Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2014
Privacy-preserving outsourced classification in cloud computing
Ping Li, Jin Li, Zhengan Huang, Chong-Zhi Gao, Wen-Bin Chen, and Kai Chen · 2017
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ZipML: Training linear models with end-to-end low precision, and a little bit of deep learning
Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, and Ce Zhang · 2017
Later among the works it cites.
Private machine learning in TensorFlow using secure computation
Morten Dahl, Jason Mancuso, Yann Dupis, Ben Decoste, Morgan Giraud, Ian Livingstone, Justin Patriquin, and Gavin Uhma · 2018
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SecureNN: Efficient and private neural network training
Sameer Wagh, Divya Gupta, and Nishanth Chandran · 2018
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ABY 3: A mixed protocol framework for machine learning
Payman Mohassel and Peter Rindal · 2018
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Cited alongside, same era.
Whispers in the hyper-space: high-bandwidth and reliable covert channel attacks inside the cloud
Zhenyu Wu, Zhang Xu, and Haining Wang · 2014
Cited alongside, same era.
Privacy preserving back-propagation neural network learning made practical with cloud computing
Jiawei Yuan and Shucheng Yu · 2014
Cited alongside, same era.
A placement vulnerability study in multi-tenant public clouds
Venkatanathan Varadarajan, Yinqian Zhang, Thomas Ristenpart, and Michael Swift · 2015
Cited alongside, same era.
Privacy-preserving deep learning
Reza Shokri and Vitaly Shmatikov · 2015
Cited alongside, same era.
Flip feng shui: Hammering a needle in the software stack
Kaveh Razavi, Ben Gras, Erik Bosman, Bart Preneel, Cristiano Giuffrida, and Herbert Bos · 2016
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
Cited alongside, same era.
Logistic regression model training based on the approximate homomorphic encryption
Andrey Kim, Yongsoo Song, Miran Kim, Keewoo Lee, and Jung Hee Cheon · 2018
Later among the works it cites.
Privacy-preserving collaborative model learning: The case of word vector training
Q. Wang, M. Du, X. Chen, Y. Chen, P. Zhou, X. Chen, and X. Huang · 2018
Later among the works it cites.
Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
Later among the works it cites.
Distributed learning without distress: Privacy-preserving empirical risk minimization
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2018
Later among the works it cites.
Empirical risk landscape analysis for understanding deep neural networks
Pan Zhou and Jiashi Feng · 2018
Later among the works it cites.
Machine learning meets computation and communication control in evolving edge and cloud: Challenges and future perspective
Tiago Koketsu Rodrigues, Katsuya Suto, Hiroki Nishiyama, Jiajia Liu, and Nei Kato · 2019
Closest in time.
Lagrange coded computing: Optimal design for resiliency, security and privacy
Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, and A Salman Avestimehr · 2019
Closest in time.
Secure computation for machine learning with SPDZ
Valerie Chen, Valerio Pastro, and Mariana Raykova · 2019
Closest in time.
Logistic regression on homomorphic encrypted data at scale
Kyoohyung Han, Seungwan Hong, Jung Hee Cheon, and Daejun Park · 2019
Closest in time.
A scalable approach for privacy-preserving collaborative machine learning
Jinhyun So, Basak Guler, and A Salman Avestimehr · 2020
Closest in time.
Analog lagrange coded computing
Mahdi Soleymani, Hessam Mahdavifar, and A Salman Avestimehr · 2020
Closest in time.
Privacy-preserving distributed learning in the analog domain
Mahdi Soleymani, Hessam Mahdavifar, and A Salman Avestimehr · 2020
Closest in time.
Codedprivateml: A fast and privacy-preserving framework for distributed machine learning
Jinhyun So, Başak Güler, and A Salman Avestimehr · 2021
Closest in time.