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\newblock Deep learning with differential privacy.
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\newblock Privacy and synthetic datasets.
\newblock {\em Stan. Tech. L. Rev.}, 22:1, 2019.

\bibitem{ding2021retiring}
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt.
\newblock Retiring adult: New datasets for fair machine learning.
\newblock {\em Advances in Neural Information Processing Systems}, 34, 2021.

\bibitem{gan}
Ian~J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David
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\newblock Generative adversarial nets.
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\bibitem{EO}
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\newblock Equality of opportunity in supervised learning.
\newblock {\em CoRR}, abs/1610.02413, 2016.

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\newblock The problem of overfitting.
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James Jordon, Daniel Jarrett, Evgeny Saveliev, Jinsung Yoon, Paul Elbers,
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\newblock Hide-and-seek privacy challenge: Synthetic data generation vs.
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\newblock In {\em NeurIPS 2020 Competition and Demonstration Track}, pages
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\bibitem{cgan}
Mehdi Mirza and Simon Osindero.
\newblock Conditional generative adversarial nets, 2014.

\bibitem{dcgan}
Alec Radford, Luke Metz, and Soumith Chintala.
\newblock Unsupervised representation learning with deep convolutional
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\bibitem{cnn}
Waseem Rawat and Zenghui Wang.
\newblock Deep convolutional neural networks for image classification: A
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\newblock {\em Neural Computation}, 29(9):2352--2449, 2017.

\bibitem{shokri2017membership}
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov.
\newblock Membership inference attacks against machine learning models.
\newblock In {\em 2017 IEEE symposium on security and privacy (SP)}, pages
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\bibitem{vgg16}
Karen Simonyan and Andrew Zisserman.
\newblock Very deep convolutional networks for large-scale image recognition,
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\bibitem{song2020overlearning}
Congzheng Song and Vitaly Shmatikov.
\newblock Overlearning reveals sensitive attributes, 2020.

\bibitem{stadler2020synthetic}
Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso.
\newblock Synthetic data-a privacy mirage.
\newblock {\em arXiv preprint arXiv:2011.07018}, 2020.

\bibitem{ctgan}
Lei Xu, Maria Skoularidou, Alfredo Cuesta-Infante, and Kalyan Veeramachaneni.
\newblock Modeling tabular data using conditional gan, 2019.

\bibitem{yeom}
Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha.
\newblock Privacy risk in machine learning: Analyzing the connection to
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\bibitem{zhifei2017cvpr}
Zhifei Zhang, Yang Song, and Hairong Qi.
\newblock Age progression/regression by conditional adversarial autoencoder.
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