Publication Type
Journal Article
Version
acceptedVersion
Publication Date
8-2024
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 17.4×17.4× for linear layer computation, and at least 1.3×1.3× 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∼4.3×2.3∼4.3× over SIMC on different state-of-the-art ML models.
Keywords
Protocols, Computational Modeling, Servers, Cryptography, Convolution, Encoding, Integrated Circuit Modeling, Garbled Circuit, Homomorphic Encryption, Privacy Protection, Secure Inference, Machine Learning Inference
Discipline
Information Security
Research Areas
Cybersecurity
Areas of Excellence
Digital transformation
Publication
IEEE Transactions on Dependable and Secure Computing
Volume
21
Issue
4
First Page
1708
Last Page
1723
ISSN
1545-5971
Identifier
10.1109/TDSC.2023.3288557
Publisher
Institute of Electrical and Electronics Engineers
Citation
XU, Guowen; HAN, Xingshuo; ZHANG, Tianwei; XU, Shengmin; NING, Jianting; HUANG, Xinyi; LI, Hongwei; and DENG, Robert H..
SIMC 2.0: Improved secure ML inference against malicious clients. (2024). IEEE Transactions on Dependable and Secure Computing. 21, (4), 1708-1723.
Available at: https://ink.library.smu.edu.sg/sis_research/9816
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TDSC.2023.3288557