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Generative machine learning models are being increasingly viewed as a way to share sensitive data between institutions.
The use of confidence or fiducial limits illustrated in the case of the binomial
Charles J Clopper and Egon S Pearson · 1934
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Nonparametric Density Estimation: The L1 View
L. Devroye and L. Gyorfi · 1985
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The foundations of cost-sensitive learning
Charles Elkan · 2001
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A probabilistic theory of pattern recognition , volume 31
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
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Membership privacy: a unifying framework for privacy definitions
Ninghui Li, Wahbeh Qardaji, Dong Su, Yi Wu, and Weining Yang · 2013
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On the statistical consistency of algorithms for binary classification under class imbalance
Aditya Menon, Harikrishna Narasimhan, Shivani Agarwal, and Sanjay Chawla · 2013
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Efficient anomaly detection by isolation using nearest neighbour ensemble
Tharindu R Bandaragoda, Kai Ming Ting, David Albrecht, Fei Tony Liu, and Jonathan R Wells · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Consistent binary classification with generalized performance metrics
Oluwasanmi O Koyejo, Nagarajan Natarajan, Pradeep K Ravikumar, and Inderjit S Dhillon · 2014
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Conditional generative adversarial nets
Mehdi Mirza and Simon Osindero · 2014
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Privacy risk in machine learning: Analyzing the connection to overfitting
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha · 2014
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Optimal bandwidth selection for kernel density functionals estimation
Su Chen · 2015
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Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Began: Boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
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A tutorial on kernel density estimation and recent advances
Yen-Chi Chen · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Towards measuring membership privacy
Yunhui Long, Vincent Bindschaedler, and Carl A Gunter · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2018
Optimal data-based binning for histograms and histogram-based probability density models
Kevin H Knuth · 2019
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Protecting gans against privacy attacks by preventing overfitting
Sumit Mukherjee, Yixi Xu, Anusua Trivedi, and Juan Lavista Ferres · 2019
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White-box vs black-box: Bayes optimal strategies for membership inference
Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Hervé Jégou · 2019
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Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Disparate vulnerability: On the unfairness of privacy attacks against machine learning
Mohammad Yaghini, Bogdan Kulynych, and Carmela Troncoso · 2019
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Towards demystifying membership inference attacks
Stacey Truex, Ling Liu, Mehmet Emre Gursoy, Lei Yu, and Wenqi Wei · 2018
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The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. scientific data 5, 180161 (aug 2018), 2018
P Tschandl, C Rosendahl, and H Kittler · 2018
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Differentially private generative adversarial network
Liyang Xie, Kaixiang Lin, Shu Wang, Fei Wang, and Jiayu Zhou · 2018
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Gan-leaks: A taxonomy of membership inference attacks against gans
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2019
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Logan: Membership inference attacks against generative models
Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro · 2019
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Monte carlo and reconstruction membership inference attacks against generative models
Benjamin Hilprecht, Martin Härterich, and Daniel Bernau · 2019
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Does learning require memorization? a short tale about a long tail
Vitaly Feldman · 2020
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Jeremy Georges-Filteau and Elisa Cirillo · 2020
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Revisiting membership inference under realistic assumptions
Bargav Jayaraman, Lingxiao Wang, David Evans, and Quanquan Gu · 2020
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Towards the infeasibility of membership inference on deep models
Shahbaz Rezaei and Xin Liu · 2020
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Protecting data privacy in the age of ai-enabled ophthalmology
Elysse Tom, Pearse A Keane, Marian Blazes, Louis R Pasquale, Michael F Chiang, Aaron Y Lee, Cecilia S Lee, and AAO Artificial Intelligence Task Force · 2020
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Par-gan: Improving the generalization of generative adversarial networks against membership inference attacks
Junjie Chen, Wendy Hui Wang, Hongchang Gao, and Xinghua Shi · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini · 2021
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Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary
Jean-Francois Rajotte, Sumit Mukherjee, Caleb Robinson, Anthony Ortiz, Christopher West, Juan M. Lavista Ferres, and Raymond T. Ng · 2021
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