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Outsourcing deep neural networks (DNNs) inference tasks to an untrusted cloud raises data privacy and integrity concerns.
On the numerical determination of the best approximations in the Chebyshev sense
L Veidinger · 1960
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Ronald L Rivest, Len Adleman, Michael L Dertouzos, et al · 1978
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Eigenvalues of covariance matrices: Application to neural-network learning
Yann Le Cun, Ido Kanter, and Sara A Solla · 1991
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Algebraic methods for interactive proof systems
Carsten Lund, Lance Fortnow, Howard Karloff, and Noam Nisan · 1992
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Timit acoustic phonetic continuous speech corpus
John S Garofolo · 1993
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A short course on approximation theory
Neal L Carothers · 1998
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The MNIST database of handwritten digits
Yann LeCun · 1998
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A privacy-preserving protocol for neural-network-based computation
Mauro Barni, Claudio Orlandi, and Alessandro Piva · 2006
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What can be approximated by polynomials with integer coefficients
Le Baron O Ferguson · 2006
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Fully homomorphic encryption using ideal lattices
Craig Gentry · 2009
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Rational approximation of real functions
Penco Petrov Petrushev and Vasil Atanasov Popov · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Efficient backprop
Yann A LeCun, Léon Bottou, Genevieve B Orr, and Klaus-Robert Müller · 2012
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Improved security for a ring-based fully homomorphic encryption scheme
Joppe W Bos, Kristin Lauter, Jake Loftus, and Michael Naehrig · 2013
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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Time-optimal interactive proofs for circuit evaluation
Justin Thaler · 2013
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Network in network
Min Lin, Qiang Chen, and Shuicheng Yan · 2014
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On the computational efficiency of training neural networks
Roi Livni, Shai Shalev-Shwartz, and Ohad Shamir · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Oblivious neural network predictions via minionn transformations
Jian Liu, Mika Juuti, Yao Lu, and Nadarajah Asokan · 2017
Later among the works it cites.
Secureml: A system for scalable privacy-preserving machine learning
Payman Mohassel and Yupeng Zhang · 2017
Later among the works it cites.
Neural networks and rational functions
Matus Telgarsky · 2017
Later among the works it cites.
Securenets: Secure inference of deep neural networks on an untrusted cloud
Xuhui Chen, Jinlong Ji, Lixing Yu, Changqing Luo, and Pan Li · 2018
Later among the works it cites.
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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Tapas: Tricks to accelerate (encrypted) prediction as a service
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Pengtao Xie, Misha Bilenko, Tom Finley, Ran Gilad-Bachrach, Kristin Lauter, and Michael Naehrig · 2014
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Delegating computation: interactive proofs for muggles
Shafi Goldwasser, Yael Tauman Kalai, and Guy N Rothblum · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Fast and accurate deep network learning by exponential linear units (ELUS)
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Globally optimal training of generalized polynomial neural networks with nonlinear spectral methods
Antoine Gautier, Quynh N Nguyen, and Matthias Hein · 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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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Amartya Sanyal, Matt Kusner, Adria Gascon, and Varun Kanade · 2018
Later among the works it cites.
Low latency privacy preserving inference
Alon Brutzkus, Ran Gilad-Bachrach, and Oren Elisha · 2019
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Nicolas Boullé, Yuji Nakatsukasa, and Alex Townsend · 2020
Closest in time.
Cryptonas: Private inference on a relu budget
Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen, and Siddharth Garg · 2020
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Amazon AWS AI
Amazon · 2021
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Secure Frameworks for Outsourced Deep Learning Inference
Zahra Ghodsi · 2021
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Google Cloud AI
Google · 2021
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Azure Machine Learning Studio
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Veriml: Enabling integrity assurances and fair payments for machine learning as a service
Lingchen Zhao, Qian Wang, Cong Wang, Qi Li, Chao Shen, and Bo Feng · 2021
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