SIMC 2.0: Improved Secure ML Inference Against Malicious Clients

Guowen Xu, Xingshuo Han, Tianwei Zhang, Shengmin Xu, Jianting Ning, Xinyi Huang, Hongwei Li, Robert H. Deng

Abstract: In this paper, we study the problem of secure ML inference against a malicious client and a semi-trusted server such that the client only learns the inference output while the server learns nothing. This problem is first formulated by Lehmkuhl et al. with a solution (MUSE, Usenix Security’21), whose performance is then substantially improved by Chandran et al.'s work (SIMC, USENIX Security’22). However, there still exists a nontrivial gap in these efforts towards practicality, giving the challenges of overhead reduction and secure inference acceleration in an all-round way. Based on this, we propose SIMC 2.0, which complies with the underlying structure of SIMC, but significantly optimizes both the linear and non-linear layers of the model. Specifically, (1) we design a new coding method for parallel homomorphic computation between matrices and vectors. (2) We reduce the size of the garbled circuit (GC) (used to calculate non-linear activation functions, e.g., ReLU) in SIMC by about two thirds. Compared with SIMC, our experiments show that SIMC 2.0 achieves a significant speedup by up to $17.4\times$ for linear layer computation, and at least $1.3\times$ reduction of both the computation and communication overhead in the implementation of non-linear layers under different data dimensions. Meanwhile, SIMC 2.0 demonstrates an encouraging runtime boost by $2.3\sim 4.3\times$ over SIMC on different state-of-the-art ML models.