[21] Marie Douriez, Harish Doraiswamy, Juliana Freire, and Cláudio T Silva. Anonymizing nyc taxi data: Does it matter? In 2016 IEEE international conference on data science and advanced analytics (DSAA), pages 140-148. IEEE, 2016.
[22] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017.
[23] John C Duchi, Michael I Jordan, and Martin J Wainwright. Local privacy and statistical minimax rates. In 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pages 429-438. IEEE, 2013.
[24] John C Duchi, Michael I Jordan, and Martin J Wainwright. Minimax optimal procedures for locally private estimation. Journal of the American Statistical Association, 113(521):182-201, 2018.
[25] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006.
[26] 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. IEEE, 2010.
[27] Ülfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security, pages 1054–1067, 2014.
[28] Jianqing Fan and Young K Truong. Nonparametric regression with errors in variables. The Annals of Statistics, pages 1900-1925, 1993.
[29] Farhad Farokhi. Deconvoluting kernel density estimation and regression for locally differentially private data. Scientific Reports, 10(1):21361, 2020.
[30] Wei Gao and Zhi-Hua Zhou. Towards convergence rate analysis of random forests for classification. Advances in neural information processing systems, 33:9300-9311, 2020.
[31] Evarist Gine and Richard Nickl. Mathematical Foundations of Infinite-Dimensional Statistical Models. Cambridge University Press, 2021.
[32] Xin Gu, Gautam Kamath, and Zhiwei Steven Wu. Choosing public datasets for private machine learning via gradient subspace distance. arXiv preprint arXiv:2303.01256, 2023.
[33] László Győrő and Martin Kroll. On rate optimal private regression under local differential privacy. arXiv preprint arXiv:2206.00114, 2022.
[34] Jihun Hamm, Yingjun Cao, and Mikhail Belkin. Learning privately from multiparty data. In International Conference on Machine Learning, pages 555-563. PMLR, 2016.
[35] Apple Inc. Differential privacy technical overview. Technical Report Apple Inc., 2017.
[36] Matthew Joseph, Janardhan Kulkarni, Jieming Mao, and Steven Z Wu. Locally private gaussian estimation. Advances in Neural Information Processing Systems, 32, 2019.
[37] Peter Kairouz, Monica Ribero Diaz, Keith Rush, and Abhradeep Thakurta. (nearly) dimension independent private erm with adagrad rates via publicly estimated subspaces. In Conference on Learning Theory, pages 2717-2746. PMLR, 2021.
[38] Peter Kairouz, Sewoong Oh, and Pramod Viswanath. Extremal mechanisms for local differential privacy. Advances in neural information processing systems, 27, 2014.
[39] Peter Kairouz, Sewoong Oh, and Pramod Viswanath. The composition theorem for differential privacy. In International conference on machine learning, pages 1376-1385. PMLR, 2015.
[40] Michael R. Kosorok. Introduction to Empirical Processes and Semiparametric Inference. Springer Series in Statistics. Springer, New York, 2008.