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Increasing interest in privacy-preserving machine learning has led to new and evolved approaches for generating private synthetic data from undisclosed real data.
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Vladimir Vapnik · 1991
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Aad W Van der Vaart · 2000
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Victor Chernozhukov and Han Hong · 2003
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Log-Laplace distributions
Tomasz J Kozubowski and Krzysztof Podgórski · 2003
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Practical privacy: the sulq framework
Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim · 2005
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Differentially private releasing via deep generative model
Xinyang Zhang, Shouling Ji, and Ting Wang · 2006
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Testing the assumptions behind importance sampling
Siem Jan Koopman, Neil Shephard, and Drew Creal · 2009
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Preserving privacy in data mining via importance weighting
Charles Elkan · 2010
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A multiplicative weights mechanism for privacy-preserving data analysis
Moritz Hardt and Guy N Rothblum · 2010
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Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
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What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
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Differentially Private Synthetic Data: Applied Evaluations and Enhancements
Lucas Rosenblatt, Xiaoyan Liu, Samira Pouyanfar, Eduardo de Leon, Anuj Desai, and Joshua Allen · 2011
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The Bernstein-von-Mises theorem under misspecification
BJK Kleijn, AW Van der Vaart, et al · 2012
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Density Ratio Estimation in Machine Learning
Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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Differential privacy based on importance weighting
Zhanglong Ji and Charles Elkan · 2013
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UCI machine learning repository, 2013
Moshe Lichman · 2013
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Robust and private bayesian inference
Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, and Benjamin IP Rubinstein · 2014
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Differentially private distributed logistic regression using private and public data
Zhanglong Ji, Xiaoqian Jiang, Shuang Wang, Li Xiong, and Lucila Ohno-Machado · 2014
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Turing: a language for flexible probabilistic inference
Hong Ge, Kai Xu, and Zoubin Ghahramani · 2018
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Real-valued (medical) time series generation with recurrent conditional gans
Stephanie Hyland, Cristóbal Esteban, and Gunnar Rätsch · 2018
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General Bayesian updating and the loss-likelihood bootstrap
Simon P Lyddon, Chris Holmes, and Stephen Walker · 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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Differentially private generative adversarial networks for time series, continuous, and discrete open data
Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger · 2019
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Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah Gabry · 2015
Cited alongside, same era.
Privacy for free: Posterior sampling and stochastic gradient monte carlo
Yu-Xiang Wang, Stephen Fienberg, and Alex Smola · 2015
Cited alongside, same era.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Cited alongside, same era.
A general framework for updating belief distributions
Pier Bissiri, Chris Holmes, and Stephen Walker · 2016
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On the theory and practice of privacy-preserving bayesian data analysis
James Foulds, Joseph Geumlek, Max Welling, and Kamalika Chaudhuri · 2016
Cited alongside, same era.
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers
Meelis Kull, Telmo Silva Filho, and Peter Flach · 2017
Cited alongside, same era.
PrivBayes: Private data release via Bayesian networks
Jun Zhang, Graham Cormode, Cecilia M Procopiuc, Divesh Srivastava, and Xiaokui Xiao · 2017
Cited alongside, same era.
Bias correction of learned generative models using likelihood-free importance weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J Horvitz, and Stefano Ermon · 2019
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela van der Schaar · 2019
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Private selection from private candidates
Jingcheng Liu and Kunal Talwar · 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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Metropolis–Hastings generative adversarial networks
Ryan Turner, Jane Hung, Eric Frank, Yunus Saatchi, and Jason Yosinski · 2019
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A survey of differentially private generative adversarial networks
Liyue Fan · 2020
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Marcel Neunhoeffer, Zhiwei Steven Wu, and Cynthia Dwork · 2020
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Foundations of Bayesian learning from synthetic data
Harrison Wilde, Jack Jewson, Sebastian Vollmer, and Chris Holmes · 2020
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Privacy-preserving parametric inference: a case for robust statistics
Marco Avella-Medina · 2021
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Leveraging public data for practical private query release
Terrance Liu, Giuseppe Vietri, Thomas Steinke, Jonathan Ullman, and Steven Wu · 2021
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Hyperparameter tuning with renyi differential privacy
Nicolas Papernot and Thomas Steinke · 2021
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