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Machine learning is data hungry; the more data a model has access to in training, the more likely it is to perform well at inference time.
R. S. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 1901
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Multiparty differential privacy via aggregation of locally trained classifiers
M. A. Pathak, S. Rane, and B. Raj · 2010
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Poisoning attacks against support vector machines
B. Biggio, B. Nelson, and P. Laskov · 2012
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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A differentially private stochastic gradient descent algorithm for multiparty classification
A. Rajkumar and S. Agarwal · 2012
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Using innovative instructions to create trustworthy software solutions
M. Hoekstra, R. Lal, P. Pappachan, C. Rozas, V. Phegade, and J. del Cuvillo · 2013
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Privacy-preserving matrix factorization
V. Nikolaenko, S. Ioannidis, U. Weinsberg, M. Joye, N. Taft, and D. Boneh · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Convolutional neural networks for sentence classification
Y. Kim · 2014
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V C 3 VC3 : Trustworthy data analytics in the cloud using SGX
F. Schuster, M. Costa, C. Fournet, C. Gkantsidis, M. Peinado, G. Mainar-Ruiz, and M. Russinovich · 2015
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Privacy-preserving deep learning
R. Shokri and V. Shmatikov · 2015
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Is feature selection secure against training data poisoning?
H. Xiao, B. Biggio, G. Brown, G. Fumera, C. Eckert, and F. Roli · 2015
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Support vector machines under adversarial label contamination
H. Xiao, B. Biggio, B. Nelson, H. Xiao, C. Eckert, and F. Roli · 2015
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Deep learning with differential privacy
M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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Data poisoning attacks against autoregressive models
S. Alfeld, X. Zhu, and P. Barford · 2016
Cited alongside, same era.
Censoring representations with an adversary
H. Edwards and A. Storkey · 2016
Cited alongside, same era.
Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy
R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing · 2016
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Learning privately from multiparty data
J. Hamm, P. Cao, and M. Belkin · 2016
Cited alongside, same era.
Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
Cited alongside, same era.
Minimax filter: Learning to preserve privacy from inference attacks
J. Hamm · 2017
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Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
B. Hitaj, G. Ateniese, and F. Perez-Cruz · 2017
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Understanding black-box predictions via influence functions
P. W. Koh and P. Liang · 2017
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Learning to pivot with adversarial networks
G. Louppe, M. Kagan, and K. Cranmer · 2017
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SecureML: A System for Scalable Privacy-Preserving Machine Learning
P. Mohassel and Y. Zhang · 2017
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Membership inference attacks against machine learning models
R. Shokri, M. Stronati, C. Song, and V. Shmatikov · 2017
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H. B. McMahan, E. Moore, D. Ramage, and B. A. y Arcas · 2016
Cited alongside, same era.
Deepfool: A simple and accurate method to fool deep neural networks
S. Moosavi-Dezfooli, A. Fawzi, and P. Frossard · 2016
Cited alongside, same era.
Oblivious multi-party machine learning on trusted processors
O. Ohrimenko, F. Schuster, C. Fournet, A. Mehta, S. Nowozin, K. Vaswani, and M. Costa · 2016
Cited alongside, same era.
The limitations of deep learning in adversarial settings
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami · 2016
Cited alongside, same era.
Auror: Defending against poisoning attacks in collaborative deep learning systems
S. Shen, S. Tople, and P. Saxena · 2016
Cited alongside, same era.
Prochlo: Strong privacy for analytics in the crowd
A. Bittau, U. Erlingsson, P. Maniatis, I. Mironov, A. Raghunathan, D. Lie, M. Rudominer, U. Kode, J. Tinnes, and B. Seefeld · 2017
Cited alongside, same era.
Practical secure aggregation for privacy-preserving machine learning
K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth · 2017
Cited alongside, same era.
Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
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Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi · 2017
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An algorithmic framework for differentially private data analysis on trusted processors
J. Allen, B. Ding, J. Kulkarni, H. Nori, O. Ohrimenko, and S. Yekhanin · 2018
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Membership Inference Attacks Against Generative Models
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro · 2018
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Privacy-preserving machine learning as a service
E. Hesamifard, H. Takabi, M. Ghasemi, and R. N. Wright · 2018
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li · 2018
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Understanding membership inferences on well-generalized learning models
Y. Long, V. Bindschaedler, L. Wang, D. Bu, X. Wang, H. Tang, C. A. Gunter, and K. Chen · 2018
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Learning differentially private recurrent language models
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2018
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Machine learning with membership privacy using adversarial regularization
M. Nasr, R. Shokri, and A. Houmansadr · 2018
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