Paper 2026/038

Scalable Honest-majority MPC for Machine Learning from Mixed Secret Sharings

Abstract

Secure multi-party computation (MPC) provides a promising approach for privacy-preserving machine learning (ML). Existing solutions generally fall into two categories but face scalability and efficiency limitations. Protocols based on Shamir secret sharing (SS) incur high communication costs, while those relying on packed Shamir secret sharing (PS) remain largely theoretical and often require costly secret routing, especially for complex ML tasks. In this work, we propose a mixed secret sharing strategy that leverages PS sharing for non-linear layers with repeated and independent operations, and SS sharing for linear layers such as matrix multiplications. To efficiently support alternating linear and non-linear computations, we design generic conversions between SS and PS sharings and further integrate them into the corresponding ML protocols, thereby eliminating additional communication and computation overhead. Moreover, we develop efficient PS sharing-based protocols for primitive non-linear building blocks, which enable multiple non-linear operations to be executed with essentially the same communication cost as a single operation. We implement our framework for secure multi-party ML inference and conduct extensive experiments. Compared to the SOTA work LXY24 (USENIX Security '24), our approach reduces communication by $3.6$-$6.1 \times$, while achieving $1.5$-$4.3 \times$ runtime improvement in the WAN setting and comparable or up to $2.3 \times$ better performance in the LAN setting.