[Csi84] Imre Csiszár. Sanov property, generalized I-projection and a conditional limit theorem. The Annals of Probability, pages 768-793, 1984.
[ CXZX15] Rui Chen, Qian Xiao, Yu Zhang, and Jianliang Xu. Differentially private high-dimensional data publication via sampling-based inference. In ACM SIGKDD international conference on knowledge discovery and data mining, pages 129-138, 2015.
[Dat23] DataCebo. Synthetic data metrics. https://docs.sdv.dev/sdmetrics/, 2023.
[DG17] Dheeru Dua and Casey Graff. UCI machine learning repository. http://archive.ics.uci.edu/ml, 2017.
[DR14] Cynthia Dwork and Aaron Roth. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2014.
[DRV10] Cynthia Dwork, Guy N Rothblum, and Salil Vadhan. Boosting and differential privacy. In 2010 IEEE 51st Annual Symposium on Foundations of Computer Science, pages 51-60, 2010.
[DWCS19] Mario Diaz, Hao Wang, Flavio P Calmon, and Lalitha Sankar. On the robustness of information-theoretic privacy measures and mechanisms. IEEE Transactions on Information Theory, 66(4):1949-1978, 2019.
[DZ96] Amir Dembo and Ofer Zeitouni. Refinements of the Gibbs conditioning principle. Probability Theory and Related Fields, 104:1-14, 1996.
[Fac20] Facebook AI. Introducing Opacus: A high-speed library for training PyTorch models with differential privacy. https://ai.facebook.com/blog/introducing-opacus-a-high-speed-library-for-training-pytorch-  $\backslash$  models-with-differential-privacy/, 2020.
$\left[\mathrm{GAH}^{+}14\right]$  Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, and Zhiwei Steven Wu. Dual query: Practical private query release for high dimensional data. In International Conference on Machine Learning, pages 1170-1178. PMLR, 2014.
[GMHI20] Chang Ge, Shubhankar Mohapatra, Xi He, and Ihab F Ilyas. Kamino: Constraint-aware differentially private data synthesis. arXiv preprint arXiv:2012.15713, 2020.
[GODC22] Georgi Ganev, Bristena Oprisanu, and Emiliano De Cristofaro. Robin Hood and Matthew effects: Differential privacy has disparate impact on synthetic data. In International Conference on Machine Learning, pages 6944–6959. PMLR, 2022.
[HBMAL19] Naoise Holohan, Stefano Braghin, Pol Mac Aonghusa, and Killian Levacher. Diff-privlib: the IBM differential privacy library. arXiv preprint arXiv:1907.02444, 2019.
$\left[\mathrm{HCZ}^{+}22\right]$  Max Hort, Zhenpeng Chen, Jie M Zhang, Federica Sarro, and Mark Harman. Bias mitigation for machine learning classifiers: A comprehensive survey. arXiv preprint arXiv:2207.07068, 2022.
[HLM12] Moritz Hardt, Katrina Ligett, and Frank McSherry. A simple and practical algorithm for differentially private data release. Advances in neural information processing systems, 25, 2012.
[HZCH20] Yifan Hu, Siqi Zhang, Xin Chen, and Niao He. Biased stochastic first-order methods for conditional stochastic optimization and applications in meta learning. Advances in Neural Information Processing Systems, 33:2759-2770, 2020.
[IBM23] IBM Research. Synderella. https://research.ibm.com/projects/synderella, 2023.
$\left[\mathrm{JSH}^{+}22\right]$  James Jordon, Lukasz Szpruch, Florimond Houssiau, Mirko Bottarelli, Giovanni Cherubin, Carsten Maple, Samuel N Cohen, and Adrian Weller. Synthetic data-what, why and how? arXiv preprint arXiv:2205.03257, 2022.