Cloud Computing Architecture Based on Network Security Vulnerabilities and Privacy Protection in the Kinematic Model of Hybrid Robots

Abstract

Aiming at the data transmission security of medical service hybrid robots and the privacy leakage of cloud data storage, this paper applies a comprehensive solution combining convolutional neural networks and kinematic models. Global and local coordinate systems are established to uniformly represent the relative position and posture of the robot end effector and each joint, and convolutional neural networks are used to improve the robot’s environmental perception and navigation efficiency. While improving the robot’s adaptability, the privacy budget of differential privacy is used to add data noise effects, and the AES (Advanced Encryption Standard) encryption algorithm is used to protect sensitive data. During data transmission, SSL/TLS (Secure Sockets Layer/Transport Layer Security) protocols are used to ensure data security. In order to improve system performance and scalability, microservice architecture and containerization technology are used to achieve service decoupling and independent deployment, and optimize the response time and processing efficiency of cloud computing architecture. Experimental results show that the average network security vulnerability detection rate of the system for 7 security areas is 0.71, and the probability of data leakage is only 1% when the privacy budget is 0.1. This method successfully guarantees data security and user privacy.