[25] E. Scornet, G. Biau, and J. Vert. Consistency of random forests. Annals of Statistics, 43(4): 1716-1741, 2015.
[26] J. Mourtada, S. Gaiffas, and E. Scornet. Universal consistency and minimax rates for online mondrian forests. Advances in Neural Information Processing Systems 30, pages 3758-3767, 2017.
[27] C. Tang, D. Garreau, and U. von Luxburg. When do random forests fail. Advances in Neural Information Processing Systems 31, pages 2987-2997, 2018.
[28] Z.-H. Zhou. Ensemble Methods: Foundations and Algorithms. CRC Press, 2012.
[29] C. Dwork. Differential privacy. In Proceedings of the 33rd International Colloquium on Automata, Languages, and Programming, pages 1-12, 2006.
[30] A. Patil and S. Singh. Differential private random forest. In Proceedings of the 3rd International Conference on Advances in Computing, Communications and Informatics, pages 2623-2630, 2014.
[31] X. Li, B. Qin, Y.-Y. Luo, and D. Zheng. A differential privacy budget allocation algorithm based on out-of-bag estimation in random forest. Mathematics, 10(22):4338, 2022.
[32] A. Blum, C. Dwork, F. McSherry, and K. Nissim. Practical privacy: The sulq framework. In Proceedings of the 24th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pages 128-138, 2005.
[33] A. Friedman and A. Schuster. Data mining with differential privacy. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 493-502, 2010.
[34] S. Fletcher and M. Islam. Decision tree classification with differential privacy: A survey. ACM Computing Surveys, 52(4):1-33, 2019.
[35] S. Samet and A. Miri. Privacy preserving ID3 using gini index over horizontally partitioned data. In Proceedings of the 6th International Conference on Computer Systems and Applications, pages 645-651, 2008.
[36] J. Vaidya, C. Clifton, M. Kantarcioglu, and A. Patterson. Privacy-preserving decision trees over vertically partitioned data. ACM Transactions on Knowledge Discovery from Data, 2(3):1-27, 2008.
[37] S. Hoogh, B. Schoenmakers, and P. Chen. Practical secure decision tree learning in a tele-treatment application. In Proceedings of the 18th International Conference on Financial Cryptography and Data Security, pages 179-194, 2014.
[38] M. Joye and F. Salehi. Private yet efficient decision tree evaluation. In Proceedings of the 32nd Annual IFIP Conference on Data and Applications Security and Privacy, pages 243–259, 2018.
[39] Y. Li, Z. Jiang, L. Yao, X. Wang, S. Yiu, and Z. Huang. Outsourced privacy-preserving C4.5 decision tree algorithm over horizontally and vertically partitioned dataset among multiple parties. Cluster Computing, 22(1):1581-1593, 2019.
[40] C. Gentry. Fully homomorphic encryption using ideal lattices. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing, pages 169-178, 2009.
[41] L. Ducas and D. Micciancio. Fhew: Bootstrapping homomorphic encryption in less than a second. In Proceedings of the 34th Annual International Conference on the Theory and Applications of Cryptographic Techniques, pages 617-640, 2015.
[42] J. Cheon, A. Kim, M. Kim, and Y. Song. Homomorphic encryption for arithmetic of approximate numbers. In Proceedings of the 23rd International Conference on Theory and Application of Cryptology and Information Security, pages 409-437, 2017.
[43] I. Chillotti, N. Gama, M. Georgieva, and M. Izabachène. Tfhe: Fast fully homomorphic encryption over the torus. Journal of Cryptology, 33(1):34-91, 2020.
[44] R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In Proceedings of the 33rd International Conference on Machine Learning, pages 201-210, 2016.
[45] Q. Lou, W. Lu, C. Hong, and L. Jiang. Falcon: Fast spectral inference on encrypted data. Advances in Neural Information Processing Systems 33, pages 2364-2374, 2020.