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diff --git a/synthetic/bck/biblio.bib b/synthetic/bck/biblio.bib new file mode 100644 index 0000000..fa524ee --- /dev/null +++ b/synthetic/bck/biblio.bib @@ -0,0 +1,196 @@ +@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} +} |