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Machine learning algorithms are vulnerable to data poisoning attacks.
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Léon Bottou and Olivier Bousquet · 2008
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Blaine Nelson, Marco Barreno, Fuching Jack Chi, Anthony D. Joseph, Benjamin I. P. Rubinstein, Udam Saini, Charles Sutton, J. D. Tygar, and Kai Xia · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Antidote: understanding and defending against poisoning of anomaly detectors
Benjamin IP Rubinstein, Blaine Nelson, Ling Huang, Anthony D Joseph, Shing-hon Lau, Satish Rao, Nina Taft, and J Doug Tygar · 2009
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Stuart Russell and Peter Norvig · 2009
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Marco Barreno, Blaine Nelson, Anthony D Joseph, and J Doug Tygar · 2010
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Differential privacy
Cynthia Dwork · 2011
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Poisoning attacks against support vector machines
Battista Biggio, Blaine Nelson, and Pavel Laskov · 2012
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Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Qualitatively characterizing neural network optimization problems
Ian J Goodfellow, Oriol Vinyals, and Andrew M Saxe · 2014
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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A unified gradient regularization family for adversarial examples
Chunchuan Lyu, Kaizhu Huang, and Hai-Ning Liang · 2015
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Is feature selection secure against training data poisoning?
Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, and Fabio Roli · 2015
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
Andrew Slavin Ross and Finale Doshi-Velez · 2018
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Poison frogs! targeted clean-label poisoning attacks on neural networks
Ali Shafahi, W. Ronny Huang, Mahyar Najibi, Octavian Suciu, Christoph Studer, Tudor Dumitras, and Tom Goldstein · 2018
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When does machine learning FAIL? generalized transferability for evasion and poisoning attacks
Octavian Suciu, Radu Marginean, Yigitcan Kaya, Hal Daume III, and Tudor Dumitras · 2018
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Byzantine-robust distributed learning: Towards optimal statistical rates
Dong Yin, Yudong Chen, Ramchandran Kannan, and Peter Bartlett · 2018
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Analyzing federated learning through an adversarial lens
Arjun Nitin Bhagoji, Supriyo Chakraborty, Prateek Mittal, and Seraphin Calo · 2019
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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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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean · 2016
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Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
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Towards poisoning of deep learning algorithms with back-gradient optimization
Luis Muñoz González, Battista Biggio, Ambra Demontis, Andrea Paudice, Vasin Wongrassamee, Emil C. Lupu, and Fabio Roli · 2017
Cited alongside, same era.
Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
Cited alongside, same era.
Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy Liang · 2017
Cited alongside, same era.
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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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2019
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Badnets: Evaluating backdooring attacks on deep neural networks
T. Gu, K. Liu, B. Dolan-Gavitt, and S. Garg · 2019
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Evaluating differentially private machine learning in practice
Bargav Jayaraman and David Evans · 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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Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes · 2019
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Transferable clean-label poisoning attacks on deep neural nets
Chen Zhu, W. Ronny Huang, Hengduo Li, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2019
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https://www.kaggle.com/c/acquire-valued-shoppers-challenge/data
Acquire Valued Shoppers Challenge | Kaggle · 2020
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https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html
sklearn.cluster.kmeans — scikit-learn 0.22.1 documentation · 2020
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https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make{_}moons.html
sklearn.datasets.make_moons — scikit-learn 0.22.1 documentation · 2020
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https://github.com/tensorflow/privacy
TensorFlow-Privacy: Library for training machine learning models with privacy for training data · 2020
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