Paper 2024/1611

Rhombus: Fast Homomorphic Matrix-Vector Multiplication for Secure Two-Party Inference

Abstract

We present $\textit{Rhombus}$, a new secure matrix-vector multiplication (MVM) protocol in the semi-honest two-party setting, which is able to be seamlessly integrated into existing privacy-preserving machine learning (PPML) frameworks and serve as the basis of secure computation in linear layers. $\textit{Rhombus}$ adopts RLWE-based homomorphic encryption (HE) with coefficient encoding, which allows messages to be chosen from not only a field $\mathbb{F}_p$ but also a ring $\mathbb{Z}_{2^\ell}$, where the latter supports faster computation in non-linear layers. To achieve better efficiency, we develop an input-output packing technique that reduces the communication cost incurred by HE with coefficient encoding by about $21\times$, and propose a split-point picking technique that reduces the number of rotations to that sublinear in the matrix dimension. Compared to the recent protocol $\textit{HELiKs}$ by Balla and Koushanfar (CCS'23), our implementation demonstrates that $\textit{Rhombus}$ improves the whole performance of an MVM protocol by a factor of $7.4\times \sim 8\times$, and improves the end-to-end performance of secure two-party inference of ResNet50 by a factor of $4.6\times \sim 18\times$.