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Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data.
The mnist database of handwritten digits
LeCun Yann, Cortes Corinna, and J Christopher · 1998
Earlier work this paper cites.
Machine learning for medical diagnosis: history, state of the art and perspective
Igor Kononenko · 2001
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
Stochastic gradient descent with differentially private updates
Shuang Song, Kamalika Chaudhuri, and Anand D Sarwate · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Parallelizing exploration-exploitation tradeoffs in gaussian process bandit optimization
Thomas Desautels, Andreas Krause, and Joel W Burdick · 2014
Earlier work this paper cites.
The Algorithmic Foundations of Differential Privacy
C. Dwork and A. Roth · 2014
Cited alongside, same era.
The reusable holdout: Preserving validity in adaptive data analysis
Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Aaron Roth · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
A general approach to adding differential privacy to iterative training procedures
Brendan McMahan, Galen Andrew, Ilya Mironov, Nicolas Papernot, Peter Kairouz, Steve Chien, and Úlfar Erlingsson · 2018
Automatic discovery of privacy-utility pareto fronts
Brendan Avent, Javier Gonzalez, Tom Diethe, Andrei Paleyes, and Borja Balle · 2019
Later among the works it cites.
Differential privacy has disparate impact on model accuracy, 2019
Eugene Bagdasaryan and Vitaly Shmatikov · 2019
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The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song · 2019
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2019
Later among the works it cites.
TensorFlow Privacy
Google · 2019
Later among the works it cites.
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Cited alongside, same era.
Scalable private learning with pate, 2018
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
Cited alongside, same era.
Robust bi-tempered logistic loss based on bregman divergences
Ehsan Amid, Manfred KK Warmuth, Rohan Anil, and Tomer Koren · 2019
Cited alongside, same era.
Congzheng Song and Vitaly Shmatikov · 2019
Later among the works it cites.
Why gradient clipping accelerates training: A theoretical justification for adaptivity
Jingzhao Zhang, Tianxing He, Suvrit Sra, and Ali Jadbabaie · 2019
Later among the works it cites.