SIMC 2.0: Improved secure ML inference against malicious clients

• Main Authors: XU, Guowen, HAN, Xingshuo, ZHANG, Tianwei, XU, Shengmin, NING, Jianting, HUANG, Xinyi, LI, Hongwei, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Protocols; Computational Modeling; Servers; Cryptography; Convolution; Encoding; Integrated Circuit Modeling; Garbled Circuit; Homomorphic Encryption; Privacy Protection; Secure Inference; Machine Learning Inference; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9816; https://ink.library.smu.edu.sg/context/sis_research/article/10816/viewcontent/2207.04637v2.pdf
• Institution: Singapore Management University

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...