  Secure Multi-Party Computation (MPC) is an area of cryptography that enables
computation on sensitive data from multiple sources while maintaining privacy
guarantees. However, theoretical MPC protocols often do not scale efficiently
to real-world data. This project investigates the efficiency of the SPDZ
framework, which provides an implementation of an MPC protocol with malicious
security, in the context of popular machine learning (ML) algorithms. In
particular, we chose applications such as linear regression and logistic
regression, which have been implemented and evaluated using semi-honest MPC
techniques. We demonstrate that the SPDZ framework outperforms these previous
implementations while providing stronger security.
