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There is growing recognition that machine learning (ML) exposes new security and privacy vulnerabilities in software systems, yet the technical community's understanding of the nature and extent of these vulnerabilities remains limited but expanding.
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Jerome H Saltzer and Michael D Schroeder · 1975
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Data provenance: Some basic issues
Peter Buneman, Sanjeev Khanna, and Wang-Chiew Tan · 2000
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Computer security in the real world
Butler W Lampson · 2004
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Adversarial learning
Daniel Lowd and Christopher Meek · 2005
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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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Searching the searchers with searchaudit
John P John, Fang Yu, Yinglian Xie, Martín Abadi, and Arvind Krishnamurthy · 2010
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Re: Captchas-understanding captcha-solving services in an economic context
Marti Motoyama, Kirill Levchenko, Chris Kanich, Damon McCoy, Geoffrey M Voelker, and Stefan Savage · 2010
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Ml confidential: Machine learning on encrypted data
Thore Graepel, Kristin Lauter, and Michael Naehrig · 2012
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Evasion attacks against machine learning at test time
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli · 2013
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Machine learning for market microstructure and high frequency trading
Michael Kearns and Yuriy Nevmyvaka · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2013
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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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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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Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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The mythos of model interpretability
Zachary C Lipton · 2016
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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, et al · 2016
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Semi-supervised knowledge transfer for deep learning from private training data
Nicolas Papernot, Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 2016
Deepxplore: Automated whitebox testing of deep learning systems
Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana · 2017
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Foolbox v0. 8.0: A python toolbox to benchmark the robustness of machine learning models
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Ensemble adversarial training: Attacks and defenses
Florian Tramèr, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel · 2017
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cleverhans v2. 0.0: an adversarial machine learning library
Nicolas Papernot, Nicholas Carlini, Ian Goodfellow, Reuben Feinman, Fartash Faghri, Alexander Matyasko, Karen Hambardzumyan, Yi-Lin Juang, Alexey Kurakin, Ryan Sheatsley, et al · 2016
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Stealing machine learning models via prediction apis
Florian Tramèr, Fan Zhang, Ari Juels, Michael K Reiter, and Thomas Ristenpart · 2016
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On the protection of private information in machine learning systems: Two recent approches
Martín Abadi, Ulfar Erlingsson, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, and Li Zhang · 2017
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Prochlo: Strong privacy for analytics in the crowd
Andrea Bittau, Ulfar Erlingsson, Petros Maniatis, Ilya Mironov, Ananth Raghunathan, David Lie, Mitch Rudominer, Ushasree Kode, Julien Tinnes, and Bernhard Seefeld · 2017
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Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Wild patterns: Ten years after the rise of adversarial machine learning
Battista Biggio and Fabio Roli · 2018
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The secret sharer: Measuring unintended neural network memorization & extracting secrets
Nicholas Carlini, Chang Liu, Jernej Kos, Úlfar Erlingsson, and Dawn Song · 2018
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Tensorfuzz: Debugging neural networks with coverage-guided fuzzing
Augustus Odena and Ian Goodfellow · 2018
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Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning
Nicolas Papernot and Patrick McDaniel · 2018
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Sok: Security and privacy in machine learning
Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael P Wellman · 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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Evaluation methodology for attacks against confidence thresholding models
Anonymous · 2019
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