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We investigate whether Differentially Private SGD offers better privacy in practice than what is guaranteed by its state-of-the-art analysis.
Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 1912
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The use of confidence or fiducial limits illustrated in the case of the binomial
Charles J Clopper and Egon S Pearson · 1934
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Revealing information while preserving privacy
Irit Dinur and Kobbi Nissim · 2003
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Our data, ourselves: Privacy via distributed noise generation
Cynthia Dwork, Krishnaram Kenthapadi, Frank McSherry, Ilya Mironov, and Moni Naor · 2006
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays
Nils Homer, Szabolcs Szelinger, Margot Redman, David Duggan, Waibhav Tembe, Jill Muehling, John V Pearson, Dietrich A Stephan, Stanley F Nelson, and David W Craig · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Genomic privacy and limits of individual detection in a pool
Sriram Sankararaman, Guillaume Obozinski, Michael I Jordan, and Eran Halperin · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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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
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RAPPOR: Randomized aggregatable privacy-preserving ordinal response
Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova · 2014
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Robust traceability from trace amounts
Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman, and Salil Vadhan · 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.
Concentrated differential privacy: Simplifications, extensions, and lower bounds
Mark Bun and Thomas Steinke · 2016
Cited alongside, same era.
Concentrated differential privacy
Cynthia Dwork and Guy N Rothblum · 2016
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Úlfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
Cited alongside, same era.
Badnets: Identifying vulnerabilities in the machine learning model supply chain
Detecting violations of differential privacy
Zeyu Ding, Yuxin Wang, Guanhong Wang, Danfeng Zhang, and Daniel Kifer · 2018
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Property testing for differential privacy
Anna C Gilbert and Audra McMillan · 2018
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Scalable private learning with pate
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson · 2018
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2018
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Jinshuo Dong, Aaron Roth, and Weijie J Su · 2019
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Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg · 2017
Cited alongside, same era.
Utility cost of formal privacy for releasing national employer-employee statistics
Samuel Haney, Ashwin Machanavajjhala, John M Abowd, Matthew Graham, Mark Kutzbach, and Lars Vilhuber · 2017
Cited alongside, same era.
Rényi differential privacy
Ilya Mironov · 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.
Learning new words, 2017
A.G. Thakurta, A.H. Vyrros, U.S. Vaishampayan, G. Kapoor, J. Freudiger, V.R. Sridhar, and D. Davidson · 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.
Composable and versatile privacy via truncated cdp
Mark Bun, Cynthia Dwork, Guy N Rothblum, and Thomas Steinke · 2018
Cited alongside, same era.
Stephanie L Hyland and Shruti Tople · 2019
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R \ \backslash ’enyi differential privacy of the sampled gaussian mechanism
Ilya Mironov, Kunal Talwar, and Li Zhang · 2019
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Data poisoning against differentially-private learners: Attacks and defenses
Yuzhe Ma, Xiaojin Zhu, and Justin Hsu · 2019
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Differentially private model publishing for deep learning
Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, and Stacey Truex · 2019
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On the effectiveness of mitigating data poisoning attacks with gradient shaping
Sanghyun Hong, Varun Chandrasekaran, Yiğitcan Kaya, Tudor Dumitraş, and Nicolas Papernot · 2020
Closest in time.
Making the shoe fit: Architectures, initializations, and tuning for learning with privacy, 2020
Nicolas Papernot, Steve Chien, Shuang Song, Abhradeep Thakurta, and Ulfar Erlingsson · 2020
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