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We propose a differentially private data generation paradigm using random feature representations of kernel mean embeddings when comparing the distribution of true data with that of synthetic data.
Information theory and statistics: A tutorial
I. Csiszár and P.C. Shields · 2004
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Our data, ourselves: Privacy via distributed noise generation
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A. Smola, A. Gretton, L. Song, and B. Schölkopf · 2007
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2008
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Differentially private data release through multidimensional partitioning
Yonghui Xiao, Li Xiong, and Chun Yuan · 2010
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Differentially private data release for data mining
Noman Mohammed, Rui Chen, Benjamin C.M. Fung, and Philip S. Yu · 2011
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Universality, characteristic kernels and rkhs embedding of measures
Bharath K Sriperumbudur, Kenji Fukumizu, and Gert RG Lanckriet · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
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A simple and practical algorithm for differentially private data release
Moritz Hardt, Katrina Ligett, and Frank Mcsherry · 2012
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Fourier Analysis on Groups: Interscience Tracts in Pure and Applied Mathematics, No. 12
Walter Rudin · 2013
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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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Priview: practical differentially private release of marginal contingency tables
Wahbeh Qardaji, Weining Yang, and Ninghui Li · 2014
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Differentially private high-dimensional data publication via sampling-based inference
Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu · 2015
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On the error of random fourier features
Dougal J. Sutherland and Jeff Schneider · 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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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Mmd gan: Towards deeper understanding of moment matching network
Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabas Poczos · 2017
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Rényi differential privacy
Ilya Mironov · 2017
Characteristic and universal tensor product kernels
Zoltán Szabó and Bharath K. Sriperumbudur · 2018
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Wasserstein auto-encoders
Ilya Tolstikhin, Olivier Bousquet, Sylvain Gelly, and Bernhard Schoelkopf · 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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Ektelo: A framework for defining differentially-private computations
Dan Zhang, Ryan McKenna, Ios Kotsogiannis, Michael Hay, Ashwin Machanavajjhala, and Gerome Miklau · 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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Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, and Kunal Talwar · 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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Differentially private data publishing and analysis: A survey
T. Zhu, G. Li, W. Zhou, and P. S. Yu · 2017
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https://www.nist.gov/ctl/pscr/open-innovation-prize-challenges/past-prize-challenges/2018-differential-privacy-synthetic
Nist 2018 differential privacy synthetic data challenge · 2018
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Differentially private database release via kernel mean embeddings
Matej Balog, Ilya Tolstikhin, and Bernhard Schölkopf · 2018
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Joint generative moment-matching network for learning structural latent code
Hongchang Gao and Heng Huang · 2018
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Ryan McKenna, Daniel Sheldon, and Gerome Miklau · 2019
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Differentially private distributed data summarization under covariate shift
Kanthi Sarpatwar, Karthikeyan Shanmugam, Venkata Sitaramagiridharganesh Ganapavarapu, Ashish Jagmohan, and Roman Vaculin · 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
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
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Subsampled renyi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
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PATE-GAN: Generating synthetic data with differential privacy guarantees
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2019
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Generative adversarial networks with joint distribution moment matching
Yi-Ying Zhang, Chao-Min Shen, Hao Feng, Preston Thomas Fletcher, and Gui-Xu Zhang · 2019
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Gs-wgan: A gradient-sanitized approach for learning differentially private generators
Dingfan Chen, Tribhuvanesh Orekondy, and Mario Fritz · 2020
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