[46] Z. Ghodsi, N. Jha, B. Reagen, and S. Garg. Circa: Stochastic RLUs for private deep learning. Advances in Neural Information Processing Systems 34, pages 2241-2252, 2021.
[47] X. Li, R. Dowsley, and M. Cock. Privacy-preserving feature selection with secure multiparty computation. In Proceedings of the 38th International Conference on Machine Learning, pages 6326-6336, 2021.
[48] A. Aloufi, P. Hu, H. Wong, and S. Chow. Blindfolded evaluation of random forests with multi-key homomorphic encryption. IEEE Transactions on Dependable and Secure Computing, 18(4):1821-1835, 2019.
[49] J. Li, X. Kuang, S. Lin, X. Ma, and Y. Tang. Privacy preservation for machine learning training and classification based on homomorphic encryption schemes. Information Sciences, 526: 166-179, 2020.
[50] L. Pulido-Gaytan, A. Tchernykh, J. Cortés-Mendoza, M. Babenko, and G. Radchenko. A survey on privacy-preserving machine learning with fully homomorphic encryption. In Proceedings of the 7th Latin American Conference on High Performance Computing, pages 115-129, 2021.
[51] A. Akavia, M. Leibovich, Y. Resheff, R. Ron, M. Shahar, and M. Vald. Privacy-preserving decision trees training and prediction. ACM Transactions on Privacy and Security, 25(3):1-30, 2022.
[52] K. Cong, D. Das, J. Park, and H. Pereira. Sortinghat: Efficient private decision tree evaluation via homomorphic encryption and transciphering. In Proceedings of the 29th ACM SIGSAC Conference on Computer and Communications Security, pages 563-577, 2022.
[53] J. Brickell, D. Porter, V. Shmatikov, and E. Witchel. Privacy-preserving remote diagnostics. In Proceedings of the 14th ACM SIGSAC Conference on Computer and Communications Security, pages 498-507, 2007.
[54] M. Barni, P. Failla, V. Kolesnikov, R. Lazzeretti, A. Sadeghi, and T. Schneider. Secure evaluation of private linear branching programs with medical applications. In Proceedings of the 17th European Symposium on Research in Computer Security, pages 424-439, 2009.
[55] R. Tai, J. Ma, Y. Zhao, and S. Chow. Privacy-preserving decision trees evaluation via linear functions. In Proceedings of the 22nd European Symposium on Research in Computer Security, pages 494-512, 2017.
[56] A. Tueno, F. Kerschbaum, and S. Katzenbeisser. Private evaluation of decision trees using sublinear cost. Proceedings on Privacy Enhancing Technologies, 2019(1):266-286, 2019.
[57] Á. Kiss, M. Naderpour, J. Liu, N. Asokan, and T. Schneider. Sok: Modular and efficient private decision tree evaluation. In Proceedings of Privacy Enhancing Technologies, pages 187-208, 2019.
[58] K. Sarpatwar, N. Ratha, K. Nandakumar, K. Shanmugam, J. Rayfield, S. Pankanti, and R. Vaculin. Privacy enhanced decision tree inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 154–159, 2020.
[59] K. LeFevre, D. DeWitt, and R. Ramakrishnan. Mondrian multidimensional k-anonymity. In Proceedings of the 22nd International Conference on Data Engineering, pages 25-25, 2006.
[60] L. Sweeney. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 10(05):557-570, 2002.
[61] W. Du and Z. Zhan. Building decision tree classifier on private data. In Proceedings of the 14th IEEE International Conference on Privacy, Security and Data Mining, pages 1-8, 2002.
[62] Y. Wu, S. Cai, X. Xiao, G. Chen, and B. Ooi. Privacy preserving vertical federated learning for tree-based models. CoRR/abstract, 2008.06170, 2020.
[63] K. Hamada, D. Ikarashi, R. Kikuchi, and K. Chida. Efficient decision tree training with new data structure for secure multi-party computation. CoRR/abstract, 2112.12906, 2021.
[64] A. Aminifar, F. Rabbi, K. Pun, and Y. Lamo. Privacy preserving distributed extremely randomized trees. In Proceedings of the 36th Annual ACM Symposium on Applied Computing, pages 1102-1105, 2021.
[65] M. De Cock, R. Dowsley, C. Horst, R. Katti, A. Nascimento, W. Poon, and S. Truex. Efficient and private scoring of decision trees, support vector machines and logistic regression models