1. Privacy Preserving Synthetic Data Release Using Deep Learning
2. Hassan Jameel Asghar , Ming Ding , Thierry Rakotoarivelo , Sirine Mrabet , and Mohamed Ali Kâafar . 2019. Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula. CoRR abs/1902.01499 ( 2019 ). arXiv:1902.01499 http://arxiv.org/abs/1902.01499 Hassan Jameel Asghar, Ming Ding, Thierry Rakotoarivelo, Sirine Mrabet, and Mohamed Ali Kâafar. 2019. Differentially Private Release of High-Dimensional Datasets using the Gaussian Copula. CoRR abs/1902.01499 (2019). arXiv:1902.01499 http://arxiv.org/abs/1902.01499
3. Sergul Aydore , William Brown , Michael Kearns , Krishnaram Kenthapadi , Luca Melis , Aaron Roth , and Ankit A Siva . 2021 . Differentially Private Query Release Through Adaptive Projection . In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research), Marina Meila and Tong Zhang (Eds.) , Vol. 139 . PMLR, 457--467. https://proceedings.mlr.press/v139/aydore21a.html Sergul Aydore, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, and Ankit A Siva. 2021. Differentially Private Query Release Through Adaptive Projection. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research), Marina Meila and Tong Zhang (Eds.), Vol. 139. PMLR, 457--467. https://proceedings.mlr.press/v139/aydore21a.html
4. Plausible deniability for privacy-preserving data synthesis
5. Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds