MD-SONIC: Maliciously-Secure Outsourcing Neural Network Inference With Reduced Online Communication

作者：Yansong Zhang, Xiaojun Chen, Ye Dong, Qinghui Zhang, Rui Hou, Qiang Liu, Xudong Chen · 发表于：IEEE Transactions on Information Forensics and Security · 年份：2025 · DOI：10.1109/tifs.2025.3550834 · 被引用次数：3 · 研究领域：Adversarial Robustness in Machine Learning、Network Security and Intrusion Detection、Anomaly Detection Techniques and Applications

With the widespread deployment of Deep-Learning-as-a-Service, secure multi-party computation-based outsourcing neural network (NN) inference has garnered significant attention for its high-security guarantee. Nevertheless, under the dishonest-majority setting with malicious adversaries, prior secure inference works are still costly in terms of communication and run-time. Additionally, existing outsourcing frameworks impose a substantial client-side design, which leads to obstacles in resource-constrained devices. To address the above challenges, we propose MD-SONIC, an online efficient and maliciously-secure framework for outsourcing NN inference with a dishonest majority. We first construct communication-efficient n-party protocols for the basic primitives such as fixed-point multiplication and most significant bit extraction by combining mask-sharing and TinyOT-sharing with SPD$\mathbb {Z}_{2^{k}}$seamlessly. Then, we build fast secure blocks for the widely used NN operators, including matrix multiplication, ReLU, and Maxpool, on top of our basic primitives. To enable an arbitrary number of users to outsource the secure inference task to n computing servers, we propose a lightweight-client and fast$\Sigma $paradigm named SPIN, stemming from zero-knowledge proofs. Our SPIN can be instantiated into a set of efficient outsourcing protocols over multiple algebraic structures (e.g., finite field and ring). We also conduct extensive evaluations of MD-SONIC on various neural netwo...