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 [1] , 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 [2, 7] . We demonstrate that the SPDZ framework outperforms these previous implementations while providing stronger security.
REF is another work in the active setting that shows linear regression and logistic regression using SPDZ can match similar latency as SecureML.
Secure Computation for Machine Learning With SPDZ
ArXiv
ArXiv
Computer Science