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Machine learning models leak information about the datasets on which they are trained.
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Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition
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Moritz Hardt, Benjamin Recht, and Yoram Singer. 2015 · 2015
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Distributional smoothing with virtual adversarial training
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, and Shin Ishii. 2015 · 2015
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Privacy games: Optimal user-centric data obfuscation
Reza Shokri. 2015 · 2015
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Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
Martín Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016 · 2016
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Membership privacy in MicroRNA-based studies. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security
Michael Backes, Pascal Berrang, Mathias Humbert, and Praveen Manoharan. 2016 · 2016
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Adversarially learned inference
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville. 2016 · 2016
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Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In International Conference on Machine Learning
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016 · 2016
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Semi-supervised learning with generative adversarial networks
Augustus Odena. 2016 · 2016
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Gao Huang, Zhuang Liu, Kilian Q Weinberger, and Laurens van der Maaten. 2017 · 2017
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An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
Mateusz Koziński, Loïc Simon, and Frédéric Jurie. 2017 · 2017
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2017 · 2017
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Secureml: A system for scalable privacy-preserving machine learning. In Security and Privacy (SP), 2017 IEEE Symposium on
Payman Mohassel and Yupeng Zhang. 2017 · 2017
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Knock Knock, Who’s There? Membership Inference on Aggregate Location Data
Apostolos Pyrgelis, Carmela Troncoso, and Emiliano De Cristofaro. 2017 · 2017
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Membership inference attacks against machine learning models. In Security and Privacy (SP), 2017 IEEE Symposium on
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017 · 2017
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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 · 2018
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Chiron: Privacy-preserving Machine Learning as a Service
Tyler Hunt, Congzheng Song, Reza Shokri, Vitaly Shmatikov, and Emmett Witchel. 2018 · 2018
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AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
Jinyuan Jia and Neil Zhenqiang Gong. 2018 · 2018
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Scalable Private Learning with PATE
Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Úlfar Erlingsson. 2018 · 2018
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Stealing Hyperparameters in Machine Learning
Binghui Wang and Neil Zhenqiang Gong. 2018 · 2018
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I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators
Lingxiao Wei, Yannan Liu, Bo Luo, Yu Li, and Qiang Xu. 2018 · 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 · 2018
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