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Deep learning techniques based on neural networks have shown significant success in a wide range of AI tasks.
Note on learning rate schedules for stochastic optimization
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Gradient-based learning applied to document recognition
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Differential privacy
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Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
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Mechanism design via differential privacy
F. McSherry and K. Talwar · 2007
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Privacy-preserving logistic regression
K. Chaudhuri and C. Monteleoni · 2008
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Privacy integrated queries: An extensible platform for privacy-preserving data analysis
F. D. McSherry · 2009
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Learning in a large function space: Privacy-preserving mechanisms for svm learning
B. I. P. Rubinstein, P. L. Bartlett, L. Huang, and N. Taft · 2009
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Boosting and differential privacy
C. Dwork, G. N. Rothblum, and S. Vadhan · 2010
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Differentially private empirical risk minimization
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate · 2011
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No free lunch in data privacy
D. Kifer and A. Machanavajjhala · 2011
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On sampling, anonymization, and differential privacy or, k-anonymization meets differential privacy
N. Li, W. Qardaji, and D. Su · 2012
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Functional mechanism: Regression analysis under differential privacy
J. Zhang, Z. Zhang, X. Xiao, Y. Yang, and M. Winslett · 2012
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Stochastic gradient descent with differentially private updates
S. Song, K. Chaudhuri, and A. D. Sarwate · 2013
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Private empirical risk minimization, revisited
R. Bassily, A. D. Smith, and A. Thakurta · 2014
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The algorithmic foundations of differential privacy
C. Dwork and A. Roth · 2014
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Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Inferential privacy guarantees for differentially private mechanisms
A. Ghosh and R. Kleinberg · 2016
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Privacy odometers and filters: Pay-as-you-go composition
R. M. Rogers, S. P. Vadhan, A. Roth, and J. Ullman · 2016
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Stealing Machine Learning Models via Prediction APIs
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart · 2016
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Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
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http://caffe.berkeleyvision.org/model_zoo.html, accessed 2017
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T. van Erven and P. Harremos · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Model Inversion Attacks That Exploit Confidence Information and Basic Countermeasures
M. Fredrikson, S. Jha, and T. Ristenpart · 2015
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Why Random Reshuffling Beats Stochastic Gradient Descent
M. Gürbüzbalaban, A. Ozdaglar, and P. Parrilo · 2015
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Privacy-Preserving Deep Learning
R. Shokri and V. Shmatikov · 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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Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds
M. Bun and T. Steinke · 2016
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Model zoo · 2017
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Xception: Deep learning with depthwise separable convolutions
F. Chollet · 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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Learning differentially private language models without losing accuracy
H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang · 2017
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Membership Inference Attacks against Machine Learning Models
R. Shokri, M. Stronati, and V. Shmatikov · 2017
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Machine learning models that remember too much
C. Song, T. Ristenpart, and V. Shmatikov · 2017
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Real-valued (medical) time series generation with recurrent conditional gans
C. Esteban, S. L. Hyland, and G. Rätsch · 2018
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Differentially private releasing via deep generative model
X. Zhang, S. Ji, and T. Wang · 2018
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