[38] Ryan McKenna, Gerome Miklau, Michael Hay, and Ashwin Machanavajjhala. Hdmm: Optimizing error of high-dimensional statistical queries under differential privacy. arXiv preprint arXiv:2106.12118, 2021.
[39] Ryan McKenna, Brett Mullins, Daniel Sheldon, and Gerome Miklau. Aim: An adaptive and iterative mechanism for differentially private synthetic data. arXiv preprint arXiv:2201.12677, 2022.
[40] Ryan McKenna, Siddhant Pradhan, Daniel R Sheldon, and Gerome Miklau. Relaxed marginal consistency for differentially private query answering. Advances in Neural Information Processing Systems, 34:20696-20707, 2021.
[41] Ryan McKenna, Daniel Sheldon, and Gerome Miklau. Graphical-model based estimation and inference for differential privacy. In International Conference on Machine Learning, pages 4435-4444. PMLR, 2019.
[42] Aleksandar Nikolov. New computational aspects of discrepancy theory. PhD thesis, Rutgers University-Graduate School-New Brunswick, 2014.
[43] Wahbeh Qardaji, Weining Yang, and Ninghui Li. Priview: practical differentially private release of marginal contingency tables. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pages 1435-1446, 2014.
[44] Giuseppe Vietri, Grace Tian, Mark Bun, Thomas Steinke, and Steven Wu. New oracle-efficient algorithms for private synthetic data release. In International Conference on Machine Learning, pages 9765-9774. PMLR, 2020.
[45] Royce Wilson, Celia Yuxin Zhang, William Lam, Damien Desfontaines, Daniel Simmons-Marengo, and Bryant Gipson. Differentially private sql with bounded user contribution. In Proceedings on Privacy Enhancing Technologies Symposium, 2020.
[46] Yingtai Xiao, Zeyu Ding, Yuxin Wang, Danfeng Zhang, and Daniel Kifer. Optimizing fitness-for-use of differentially private linear queries. In VLDB, 2021.
[47] Yingtai Xiao, Guanhong Wang, Danfeng Zhang, and Daniel Kifer. Answering private linear queries adaptively using the common mechanism. https://arxiv.org/abs/2212.00135, 2022.
[48] Chugui Xu, Ju Ren, Yaoxue Zhang, Zhan Qin, and Kui Ren. Dppro: Differentially private high-dimensional data release via random projection. IEEE Transactions on Information Forensics and Security, 12(12):3081-3093, 2017.
[49] Grigory Yaroslavtsev, Graham Cormode, Cecilia M Procopiuc, and Divesh Srivastava. Accurate and efficient private release of datacubes and contingency tables. In 2013 IEEE 29th International Conference on Data Engineering (ICDE), pages 745-756. IEEE, 2013.
[50] Fei Yu, Stephen E Fienberg, Aleksandra B Slavković, and Caroline Uhler. Scalable privacy-preserving data sharing methodology for genome-wide association studies. Journal of biomedical informatics, 50:133-141, 2014.
[51] Ganzhao Yuan, Yin Yang, Zhenjie Zhang, and Zhifeng Hao. Convex optimization for linear query processing under approximate differential privacy. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
[52] Ganzhao Yuan, Zhenjie Zhang, Marianne Winslett, Xiaokui Xiao, Yin Yang, and Zhifeng Hao. Low-rank mechanism: Optimizing batch queries under differential privacy. Proc. VLDB Endow., 5(11):1352-1363, July 2012.
[53] Ganzhao Yuan, Zhenjie Zhang, Marianne Winslett, Xiaokui Xiao, Yin Yang, and Zhifeng Hao. Optimizing batch linear queries under exact and approximate differential privacy. ACM Transactions on Database Systems (TODS), 40(2):1-47, 2015.
[54] Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, and Xiaokui Xiao. Privbayes: Private data release via bayesian networks. ACM Trans. Database Syst., 42(4), oct 2017.
[55] Wei Zhang, Jingwen Zhao, Fengqiong Wei, and Yunfang Chen. Differentially private high-dimensional data publication via markov network. EAI Endorsed Transactions on Security and Safety, 6(19), 2019.
[56] Zhikun Zhang, Tianhao Wang, Jean Honorio, Ninghui Li, Michael Backes, Shibo He, Jiming Chen, and Yang Zhang. Privsyn: Differentially private data synthesis. 2021.