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We propose a new framework of synthesizing data using deep generative models in a differentially private manner.
A test of goodness of fit for symmetric random variables
CE Heathcote · 1972
Earlier work this paper cites.
A survey of the theory of characteristic functions
Eugene Lukacs · 1972
Earlier work this paper cites.
Probability with martingales
David Williams · 1991
Earlier work this paper cites.
Bayesian neural networks and density networks
David J.C MacKay · 1995
Earlier work this paper cites.
Differential privacy
Cynthia Dwork · 2006
Earlier work this paper cites.
Uci machine learning repository, 2007
Arthur Asuncion and David Newman · 2007
Earlier work this paper cites.
Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
Earlier work this paper cites.
A firm foundation for private data analysis
Cynthia Dwork · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
Universality, characteristic kernels and rkhs embedding of measures
Bharath K Sriperumbudur, Kenji Fukumizu, and Gert RG Lanckriet · 2011
Earlier work this paper cites.
Probability theory: a comprehensive course
Achim Klenke · 2013
Earlier work this paper cites.
The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
Earlier work this paper cites.
Geometric measure theory
Herbert Federer · 2014
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Priview: practical differentially private release of marginal contingency tables
Wahbeh Qardaji, Weining Yang, and Ninghui Li · 2014
Earlier work this paper cites.
Differentially private high-dimensional data publication via sampling-based inference
Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu · 2015
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Fast two-sample testing with analytic representations of probability measures
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Andrea Dal Pozzolo, Olivier Caelen, Reid A Johnson, and Gianluca Bontempi · 2015
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Xi He, Graham Cormode, Ashwin Machanavajjhala, Cecilia M Procopiuc, and Divesh Srivastava · 2015
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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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Lorenzo Frigerio, Anderson Santana de Oliveira, Laurent Gomez, and Patrick Duverger · 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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Dp-cgan: Differentially private synthetic data and label generation
Reihaneh Torkzadehmahani, Peter Kairouz, and Benedict Paten · 2019
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Subsampled rényi differential privacy and analytical moments accountant
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Modeling tabular data using conditional gan
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni · 2019
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A characteristic function approach to deep implicit generative modeling
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Rényi differential privacy
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DP-EM: differentially private expectation maximization
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Gs-wgan: A gradient-sanitized approach for learning differentially private generators
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Auditing differentially private machine learning: How private is private sgd?
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Reciprocal adversarial learning via characteristic functions
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Data synthesis via differentially private markov random fields
Kuntai Cai, Xiaoyu Lei, Jianxin Wei, and Xiaokui Xiao · 2021
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Adversary instantiation: Lower bounds for differentially private machine learning
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PrivSyn: Differentially private data synthesis
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