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Differentially private learning on real-world data poses challenges for standard machine learning practice: privacy guarantees are difficult to interpret, hyperparameter tuning on private data reduces the privacy budget, and ad-hoc privacy attacks are often required to test model privacy.
Differential privacy
Cynthia Dwork · 2006
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A stability-based validation procedure for differentially private machine learning
Kamalika Chaudhuri and Staal A Vinterbo · 2013
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Deep Learning with Differential Privacy
M. Abadi, A. Chu, I. Goodfellow, H. Brendan McMahan, I. Mironov, K. Talwar, and L. Zhang · 2016
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On-average kl-privacy and its equivalence to generalization for max-entropy mechanisms
Yu-Xiang Wang, Jing Lei, and Stephen E Fienberg · 2016
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2016
Cited alongside, same era.
Accurate, large minibatch SGD: training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross B. Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Cited alongside, same era.
Deep models under the GAN: information leakage from collaborative deep learning
Briland Hitaj, Giuseppe Ateniese, and Fernando Pérez-Cruz · 2017
Cited alongside, same era.
Learning differentially private language models without losing accuracy
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2017
Cited alongside, same era.
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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Differentially private model selection with penalized and constrained likelihood
Jing Lei, Anne-Sophie Charest, Aleksandra Slavkovic, Adam Smith, and Stephen Fienberg · 2018
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A Practical Approach to Differential Private Learning
Koen Lennart van der Veen · 2018
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