Paper 2025/1551

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

Abstract

Secure two-party machine learning has made substantial progress through the use of mixed-mode protocols. Despite these advancements, existing approaches often suffer from efficiency bottlenecks due to the inherent mismatch between the optimal domains of various cryptographic primitives, such as Homomorphic Encryption and Oblivious Transfer. In response to these challenges, we introduce the \tNAME{} framework, which features an efficient modulus conversion protocol. This breakthrough enables seamless integration of the most suitable cryptographic subprotocols within their optimal modulus domains, allowing linear computations to be executed over a prime modulus and nonlinear computations over a two-power modulus, with a minimal modulus conversion overhead. We further establish new benchmarks for the performance of fundamental primitives, namely comparison and multiplication, across various two-party techniques, together with our practical optimizations to improve efficiency. By incorporating these techniques, \tNAME{} demonstrates significant performance enhancements over state-of-the-art solutions: \romannumeral1) we report a $6\times$-$100\times$ improvement for approximated truncations with 1-bit error tolerance; \romannumeral2) an average of $5\times$ (resp. $4\times$) reduction in communication (resp. runtime) for machine learning functions; \romannumeral3) and a 25\%-99\% improvement in cost-efficiency for private inference of deep neural networks and 50\% improvement in private training of gradient boosting decision trees.