@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} }