M&M: Secure Two-Party Machine Learning Through Modulus Conversion and Mixed-Mode Protocols

Summary

The paper introduces M&M, a framework for secure two-party machine learning that uses an efficient modulus conversion protocol to run each cryptographic subprotocol in its optimal modulus domain. The authors report a 6x to 100x improvement for approximated truncations with 1-bit error tolerance, an average 5x reduction in communication and 4x reduction in runtime for machine learning functions, and a 25% to 99% cost-efficiency gain for private deep neural network inference alongside a 50% gain for private gradient boosting decision tree training.