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@misc{song2020overlearning,
title={Overlearning Reveals Sensitive Attributes},
author={Congzheng Song and Vitaly Shmatikov},
year={2020},
eprint={1905.11742},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@article{EO,
author = {Moritz Hardt and
Eric Price and
Nathan Srebro},
title = {Equality of Opportunity in Supervised Learning},
journal = {CoRR},
volume = {abs/1610.02413},
year = {2016},
url = {http://arxiv.org/abs/1610.02413},
eprinttype = {arXiv},
eprint = {1610.02413},
timestamp = {Tue, 26 Apr 2022 09:17:17 +0200},
biburl = {https://dblp.org/rec/journals/corr/HardtPS16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
@article{hawkins2004problem,
title={The problem of overfitting},
author={Hawkins, Douglas M},
journal={Journal of chemical information and computer sciences},
volume={44},
number={1},
pages={1--12},
year={2004},
publisher={ACS Publications}
}
@misc{yeom,
title={Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting},
author={Samuel Yeom and Irene Giacomelli and Matt Fredrikson and Somesh Jha},
year={2018},
eprint={1709.01604},
archivePrefix={arXiv},
primaryClass={cs.CR}
}
@misc{vgg16,
title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
author={Karen Simonyan and Andrew Zisserman},
year={2015},
eprint={1409.1556},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/1409.1556},
}
@misc{CGAN,
title={Conditional Generative Adversarial Nets},
author={Mehdi Mirza and Simon Osindero},
year={2014},
eprint={1411.1784},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1411.1784},
}
@ARTICLE{cnn,
author={Rawat, Waseem and Wang, Zenghui},
journal={Neural Computation},
title={Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review},
year={2017},
volume={29},
number={9},
pages={2352-2449},
keywords={},
doi={10.1162/neco_a_00990}}
@misc{dcgan,
title={Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks},
author={Alec Radford and Luke Metz and Soumith Chintala},
year={2016},
eprint={1511.06434},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1511.06434}
}
@inproceedings{gan,
author = {Goodfellow, Ian J. and Pouget-Abadie, Jean and Mirza, Mehdi and Xu, Bing and Warde-Farley, David and Ozair, Sherjil and Courville, Aaron and Bengio, Yoshua},
title = {Generative adversarial nets},
year = {2014},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2},
pages = {2672–2680},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'14}
}
@misc{ctgan,
title={Modeling Tabular data using Conditional GAN},
author={Lei Xu and Maria Skoularidou and Alfredo Cuesta-Infante and Kalyan Veeramachaneni},
year={2019},
eprint={1907.00503},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/1907.00503},
}
@article{bellovin2019privacy,
title={Privacy and synthetic datasets},
author={Bellovin, Steven M and Dutta, Preetam K and Reitinger, Nathan},
journal={Stan. Tech. L. Rev.},
volume={22},
pages={1},
year={2019},
publisher={HeinOnline}
}
@inproceedings{ping2017datasynthesizer,
title={Datasynthesizer: Privacy-preserving synthetic datasets},
author={Ping, Haoyue and Stoyanovich, Julia and Howe, Bill},
booktitle={Proceedings of the 29th International Conference on Scientific and Statistical Database Management},
pages={1--5},
year={2017}
}
@inproceedings{kuppa2021towards,
title={Towards improving privacy of synthetic datasets},
author={Kuppa, Aditya and Aouad, Lamine and Le-Khac, Nhien-An},
booktitle={Annual Privacy Forum},
pages={106--119},
year={2021},
organization={Springer}
}
@article{tai2023user,
title={User-Driven Synthetic Dataset Generation with Quantifiable Differential Privacy},
author={Tai, Bo-Chen and Tsou, Yao-Tung and Li, Szu-Chuang and Huang, Yennun and Tsai, Pei-Yuan and Tsai, Yu-Cheng},
journal={IEEE Transactions on Services Computing},
year={2023},
publisher={IEEE}
}
@article{stadler2020synthetic,
title={Synthetic data-A privacy mirage},
author={Stadler, Theresa and Oprisanu, Bristena and Troncoso, Carmela},
journal={arXiv preprint arXiv:2011.07018},
year={2020},
publisher={Nov}
}
@inproceedings{jordon2021hide,
title={Hide-and-seek privacy challenge: Synthetic data generation vs. patient re-identification},
author={Jordon, James and Jarrett, Daniel and Saveliev, Evgeny and Yoon, Jinsung and Elbers, Paul and Thoral, Patrick and Ercole, Ari and Zhang, Cheng and Belgrave, Danielle and van der Schaar, Mihaela},
booktitle={NeurIPS 2020 Competition and Demonstration Track},
pages={206--215},
year={2021},
organization={PMLR}
}
@inproceedings{abadi2016deep,
title={Deep learning with differential privacy},
author={Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li},
booktitle={Proceedings of the 2016 ACM SIGSAC conference on computer and communications security},
pages={308--318},
year={2016}
}
@inproceedings{shokri2017membership,
title={Membership inference attacks against machine learning models},
author={Shokri, Reza and Stronati, Marco and Song, Congzheng and Shmatikov, Vitaly},
booktitle={2017 IEEE symposium on security and privacy (SP)},
pages={3--18},
year={2017},
organization={IEEE}
}
@article{ding2021retiring,
title={Retiring Adult: New Datasets for Fair Machine Learning},
author={Ding, Frances and Hardt, Moritz and Miller, John and Schmidt, Ludwig},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021}
}
@inproceedings{zhifei2017cvpr,
title={Age Progression/Regression by Conditional Adversarial Autoencoder},
author={Zhang, Zhifei and Song, Yang and Qi, Hairong},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2017},
organization={IEEE}
}
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