Privacy-preserving training of tree ensembles over continuous data

[1] R. Cramer, I. Damgård, and J. B. Nielsen, Secure Multiparty Computation and Secret Sharing. Cambridge University Press, 2015.10.1017/CBO9781107337756 Search in Google Scholar

[2] P. Mohassel and Y. Zhang, “SecureML: A system for scalable privacy-preserving machine learning,” in IEEE Symposium on Security and Privacy (SP), pp. 19–38, 2017.10.1109/SP.2017.12 Search in Google Scholar

[3] S. Wagh, D. Gupta, and N. Chandran, “SecureNN: 3-party secure computation for neural network training,” Proc. on Privacy Enhancing Technologies, no. 3, pp. 26–49, 2019.10.2478/popets-2019-0035 Search in Google Scholar

[4] M. De Cock, R. Dowsley, A. C. A. Nascimento, D. Railsback, J. Shen, and A. Todoki, “High performance logistic regression for privacy-preserving genome analysis,” BMC Medical Genomics, vol. 14(23), 2021.10.1186/s12920-020-00869-9 Search in Google Scholar

[5] C. Guo, A. Hannun, B. Knott, L. van der Maaten, M. Tygert, and R. Zhu, “Secure multiparty computations in floating-point arithmetic,” arXiv:2001.03192, 2020. Search in Google Scholar

[6] T. G. Dietterich, “Ensemble methods in machine learning,” in International Workshop on Multiple Classifier Systems, vol. 1857 of LNCS, pp. 1–15, Springer, 2000. Search in Google Scholar

[7] D. J. Wu, T. Feng, M. Naehrig, and K. E. Lauter, “Privately evaluating decision trees and random forests.,” Proc. on Privacy Enhancing Technologies, no. 4, pp. 335–355, 2016.10.1515/popets-2016-0043 Search in Google Scholar

[8] K. Fritchman, K. Saminathan, R. Dowsley, T. Hughes, M. De Cock, A. Nascimento, and A. Teredesai, “Privacy-preserving scoring of tree ensembles: A novel framework for AI in healthcare,” in IEEE Big Data, pp. 2413–2422, 2018. Search in Google Scholar

[9] Á. Kiss, M. Naderpour, J. Liu, N. Asokan, and T. Schneider, “Sok: Modular and efficient private decision tree evaluation,” Proc. on Privacy Enhancing Technologies, no. 2, p. 187–208, 2019.10.2478/popets-2019-0026 Search in Google Scholar

[10] J. R. Quinlan, “Induction of decision trees,” Machine learning, vol. 1, no. 1, pp. 81–106, 1986.10.1007/BF00116251 Search in Google Scholar

[11] Y. Lindell and B. Pinkas, “Privacy preserving data mining,” in Annual International Cryptology Conf., pp. 36–54, 2000.10.1007/3-540-44598-6_3 Search in Google Scholar

[12] J. Vaidya and C. Clifton, “Privacy-preserving decision trees over vertically partitioned data,” in IFIP Annual Conf. on Data and Appl. Security and Privacy, pp. 139–152, 2005.10.1007/11535706_11 Search in Google Scholar

[13] S. Samet and A. Miri, “Privacy preserving ID3 using Gini index over horizontally partitioned data,” in 2008 IEEE/ACS Intern. Conf. on Comp. Syst. and Appl., pp. 645–651, 2008.10.1109/AICCSA.2008.4493598 Search in Google Scholar

[14] S. de Hoogh, B. Schoenmakers, P. Chen, and H. op den Akker, “Practical secure decision tree learning in a teletreatment application,” in Intern. Conf. on Financial Cryptography and Data Security, pp. 179–194, Springer, 2014.10.1007/978-3-662-45472-5_12 Search in Google Scholar

[15] M.-J. Xiao, K. Han, L.-S. Huang, and J.-Y. Li, “Privacy preserving C4.5 algorithm over horizontally partitioned data,” in Fifth International Conference on Grid and Cooperative Computing (GCC’06), pp. 78–85, IEEE, 2006.10.1109/GCC.2006.73 Search in Google Scholar

[16] Y. Shen, H. Shao, and L. Yang, “Privacy preserving C4.5 algorithm over vertically distributed datasets,” in Intern. Conf. on Networks Security, Wireless Communications and Trusted Computing, vol. 2, pp. 446–448, IEEE, 2009.10.1109/NSWCTC.2009.253 Search in Google Scholar

[17] G. Behera, “Privacy preserving C4.5 using Gini index,” in 2nd National Conference on Emerging Trends and Applications in Computer Science, pp. 1–4, 2011.10.1109/NCETACS.2011.5751385 Search in Google Scholar

[18] M. Abspoel, D. Escudero, and N. Volgushev, “Secure training of decision trees with continuous attributes,” in Proc. on Privacy Enhancing Technologies, no. 1, pp. 167–187, 2021.10.2478/popets-2021-0010 Search in Google Scholar

[19] J. R. Quinlan, C4.5: programs for machine learning. Elsevier, 2014. Search in Google Scholar

[20] P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine Learning, vol. 63, no. 1, pp. 3–42, 2006.10.1007/s10994-006-6226-1 Search in Google Scholar

[21] K. Deforth, M. Desgroseilliers, N. Gama, M. Georgieva, D. Jetchev, and M. Vuille, “XORBoost: Tree boosting in the multiparty computation setting.” Cryptology ePrint Archive, Report 2021/432, 2021. https://eprint.iacr.org/2021/432. Search in Google Scholar

[22] M. De Cock, R. Dowsley, C. Horst, R. Katti, A. Nascimento, W.-S. Poon, and S. Truex, “Efficient and private scoring of decision trees, support vector machines and logistic regression models based on pre-computation,” IEEE Transactions on Dependable and Secure Computing, vol. 16, no. 2, pp. 217–230, 2019.10.1109/TDSC.2017.2679189 Search in Google Scholar

[23] D. Beaver, “Commodity-based cryptography,” in STOC, vol. 97, pp. 446–455, 1997.10.1145/258533.258637 Search in Google Scholar

[24] B. David, R. Dowsley, R. Katti, and A. C. Nascimento, “Efficient unconditionally secure comparison and privacy preserving machine learning classification protocols,” in International Conference on Provable Security, pp. 354–367, Springer, 2015.10.1007/978-3-319-26059-4_20 Search in Google Scholar

[25] M. De Cock, R. Dowsley, A. C. A. Nascimento, and S. C. Newman, “Fast, privacy preserving linear regression over distributed datasets based on pre-distributed data,” in 8th ACM Workshop on Artificial Intelligence and Security (AISec), pp. 3–14, 2015.10.1145/2808769.2808774 Search in Google Scholar

[26] R. Canetti, “Universally composable security: A new paradigm for cryptographic protocols,” in 42nd Annual Symposium on Foundations of Computer Science, pp. 136–145, IEEE Computer Society, 2001.10.1109/SFCS.2001.959888 Search in Google Scholar

[27] D. Reich, A. Todoki, R. Dowsley, M. De Cock, and A. Nascimento, “Privacy-preserving classification of personal text messages with secure multi-party computation,” in Advances in Neural Information Processing Systems (NeurIPS), pp. 3752–3764, 2019. Search in Google Scholar

[28] N. Attrapadung, G. Hanaoka, S. Kiyomoto, T. Mimoto, and J. C. N. Schuldt, “A taxonomy of secure two-party comparison protocols and efficient constructions,” IEICE Trans. Fundam. Electron. Commun. Comput. Sci., vol. 102-A, no. 9, pp. 1048–1060, 2019. Search in Google Scholar

[29] D. Bogdanov, S. Laur, and J. Willemson, “Sharemind: A framework for fast privacy-preserving computations,” in European Symposium on Research in Computer Security, pp. 192–206, Springer, 2008.10.1007/978-3-540-88313-5_13 Search in Google Scholar

[30] T. Nishide and K. Ohta, “Multiparty computation for interval, equality, and comparison without bit-decomposition protocol,” in International Workshop on Public Key Cryptography, pp. 343–360, Springer, 2007.10.1007/978-3-540-71677-8_23 Search in Google Scholar

[31] L. Breiman, “Random forests,” Machine learning, vol. 45, no. 1, pp. 5–32, 2001.10.1023/A:1010933404324 Search in Google Scholar

[32] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. Search in Google Scholar

[33] S. Garcia, J. Luengo, J. A. Sáez, V. Lopez, and F. Herrera, “A survey of discretization techniques: Taxonomy and empirical analysis in supervised learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 4, pp. 734–750, 2012.10.1109/TKDE.2012.35 Search in Google Scholar

[34] R. Dowsley, Cryptography Based on Correlated Data: Foundations and Practice. PhD thesis, Karlsruhe Institute of Technology, Germany, 2016. Search in Google Scholar

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