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Neuropathic pain diagnosis simulator for causal discovery algorithm evaluation
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privgan: Protecting gans from membership inference attacks at low cost, 2019
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Design of a privacy-preserving data platform for collaboration against human trafficking, 2020
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Calibrating noise to sensitivity in private data analysis
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Vaem: a deep generative model for heterogeneous mixed type data
Chao Ma, Sebastian Tschiatschek, José Miguel Hernández-Lobato, Richard Turner, and Cheng Zhang · 2006
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Rdp-gan: Ar \ \backslash ’enyi-differential privacy based generative adversarial network
Chuan Ma, Jun Li, Ming Ding, Bo Liu, Kang Wei, Jian Weng, and H Vincent Poor · 2007
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Graphical models, exponential families, and variational inference
Martin J Wainwright and Michael Irwin Jordan · 2008
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On the complexity of differentially private data release: efficient algorithms and hardness results
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank McSherry · 2010
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Differentially private data release through multidimensional partitioning
Yonghui Xiao, Li Xiong, and Chun Yuan · 2010
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Privsyn: Differentially private data synthesis
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The algorithmic foundations of differential privacy
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Variational inference: A review for statisticians
David M Blei, Alp Kucukelbir, and Jon D McAuliffe · 2017
Monte carlo and reconstruction membership inference attacks against generative models
Benjamin Hilprecht, Martin Härterich, and Daniel Bernau · 2019
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Privacy-preserving data sharing via probabilistic modelling
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Ppgan: Privacy-preserving generative adversarial network
Yi Liu, Jialiang Peng, JQ James, and Yi Wu · 2019
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Scalable differentially private generative student model via pate
Yunhui Long, Suxin Lin, Zhuolin Yang, Carl A Gunter, and Bo Li · 2019
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Eddi: Efficient dynamic discovery of high-value information with partial vae
Chao Ma, Sebastian Tschiatschek, Konstantina Palla, Jose Miguel Hernandez-Lobato, Sebastian Nowozin, and Cheng Zhang · 2019
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Elements of causal inference: foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 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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PrivBAYES: Private data release via bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
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Protection against reconstruction and its applications in private federated learning
Abhishek Bhowmick, John Duchi, Julien Freudiger, Gaurav Kapoor, and Ryan Rogers · 2018
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Pate-gan: Generating synthetic data with differential privacy guarantees
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Privacy risk in machine learning: Analyzing the connection to overfitting
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Differentially private mixed-type data generation for unsupervised learning
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Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Generalization in generative adversarial networks: A novel perspective from privacy protection
Bingzhe Wu, Shiwan Zhao, Chaochao Chen, Haoyang Xu, Li Wang, Xiaolu Zhang, Guangyu Sun, and Jun Zhou · 2019
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Gan-leaks: A taxonomy of membership inference attacks against generative models
Dingfan Chen, Ning Yu, Yang Zhang, and Mario Fritz · 2020
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Frederik Harder, Kamil Adamczewski, and Mijung Park · 2020
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Hide-and-seek privacy challenge
James Jordon, Daniel Jarrett, Jinsung Yoon, Tavian Barnes, Paul Elbers, Patrick Thoral, Ari Ercole, Cheng Zhang, Danielle Belgrave, and Mihaela van der Schaar · 2020
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Synthetic data – a privacy mirage, 2020
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso · 2020
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Alleviating privacy attacks via causal learning
Shruti Tople, Amit Sharma, and Aditya Nori · 2020
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Diagnostic questions: The neurips 2020 education challenge
Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E Turner, Richard G Baraniuk, Craig Barton, Simon Peyton Jones, et al · 2020
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Private fl-gan: Differential privacy synthetic data generation based on federated learning
Bangzhou Xin, Wei Yang, Yangyang Geng, Sheng Chen, Shaowei Wang, and Liusheng Huang · 2020
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