Abstract: Secure multiparty computation (SMPC) enables joint computation while keeping private data undisclosed, positioning it as a core technology in privacypreserving computing. However, its high computational complexity and substantial overhead render practical deployment reliant on cloud providers for computational resources. To meet the requirement of realtime protocol monitoring in privacypreserving computing scenarios on cloud platforms, this paper proposes a neural networkbased protocol identification scheme for SMPC. By collecting performance data from computation nodes, including CPU usage and network bandwidth usage, a 3D convolutional neural network (CNN) model integrating spatiotemporal feature extraction capabilities is constructed. This model, along with a dynamic threshold mechanism, enables highaccuracy classification of known protocols and anomaly detection of unknown protocols. Experimental results show that the model attains an accuracy of 98% on the validation dataset and a detection rate exceeding 98% for unknown protocols, thereby significantly improving the operational security and reliability of SMPC systems.

Key words: secure multiparty computation, privacypreserving machine learning, cloud platform, protocol monitoring, neural network

摘要： 多方安全计算可在不泄露私有数据的前提下实现联合计算，是隐私计算的核心技术，但其高计算复杂度和高通信量使得实际部署依赖云提供商提供算力支持.针对云平台下隐私计算场景中协议实时监测的需求，提出一种基于神经网络的多方安全计算协议识别方案.通过采集计算节点的性能数据(如CPU占用率、网络带宽占用等)，构建融合时空特征提取能力的3维卷积神经网络模型，结合动态阈值机制实现已知协议的高精度分类与未知协议的异常检测.实验表明，该模型在验证数据集上准确率达98%，对未知协议的检出率超过98%，可有效提升多方安全计算系统的运行安全性与可靠性.

关键词: 多方安全计算, 隐私保护机器学习, 云平台, 协议监测, 神经网络