基于神经网络的多方安全计算协议识别方案研究OA

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.