Keller, M. (2020). MP-SPDZ: A Versatile Framework for Multi-Party Computation. Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, 1575–1590. Presented at the Virtual Event, USA. doi:10.1145/3372297.3417872.
Catrina, O., & Saxena, A. (2010). Secure Computation with Fixed-Point Numbers. In R. Sion (Ed.), Financial Cryptography and Data Security (pp. 35–50). Berlin, Heidelberg: Springer Berlin Heidelberg.
Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, Springer.
Cramer, R., Damgård, I., & Nielsen, J. B. (2015). Secure multiparty computation and secret sharing. Cambridge UP.
Hong, C., Huang, Z., Lu, W.-J., Qu, H., Ma, L., Dahl, M., & Mancuso, J. (2020). Privacy-preserving collaborative machine learning on genomic data using TensorFlow. CoRR, abs/2002.04344. Retrieved from https://arxiv.org/abs/2002.04344.
Araki, T., Barak, A., Furukawa, J., Lichter, T., Lindell, Y., Nof, A., … Weinstein, O. (2017). Optimized Honest-Majority MPC for Malicious Adversaries — Breaking the 1 Billion-Gate Per Second Barrier. 2017 IEEE Symposium on Security and Privacy (SP), 843–862. doi:10.1109/SP.2017.15
Eerikson, H., Keller, M., Orlandi, C., Pullonen, P., Puura, J., & Simkin, M. (2020). Use Your Brain! Arithmetic 3PC for Any Modulus with Active Security. In Y. Tauman Kalai, A. D. Smith, & D. Wichs (Eds.), 1st Conference on Information-Theoretic Cryptography (ITC 2020) (p. 5:1-5:24). doi:10.4230/LIPIcs.ITC.2020.5