scholarly journals Analysis and Improvement on an Authentication Protocol for IoT-Enabled Devices in Distributed Cloud Computing Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Baoyuan Kang ◽  
Yanbao Han ◽  
Kun Qian ◽  
Jianqi Du
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Authentication Protocol ◽  
Impersonation Attack ◽  
Computing Environment ◽  
Authentication Protocols ◽  
Cloud Computing Environment ◽  
Cloud Server ◽  
Distributed Cloud ◽  
The Internet Of Things

Recently, a number of authentication protocols integrated with the Internet of Things (IoT) and cloud computing have been proposed for secure access control on large-scale IoT networks. In this paper, we carefully analyze Amin et al.’s authentication protocol for IoT-enabled devices in distributed cloud computing environment and find that Amin et al.’s protocol is vulnerable to several weaknesses. The main shortcoming of Amin et al.’s protocol is in authentication phase; a malicious cloud server can counterfeit the cloud server chosen by a user, and the control server cannot find this counterfeit. To overcome the shortcomings of Amin et al.’s protocol, we propose an improved protocol. In the registration phase of the improved protocol, the pseudoidentity and real identity of a user or a cloud server are bundled up with the control server’s secret numbers. This measure can effectively prevent impersonation attack. We also compare the improved protocol with several existing authentication protocols in security and computational efficiency.

scholarly journals Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Junshu Wang ◽  
Guoming Zhang ◽  
Wei Wang ◽  
Ka Zhang ◽  
Yehua Sheng
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Medical Service ◽  
Rapid Development ◽  
Medical Knowledge ◽  
Language Models ◽  
Computing Environment ◽  
Computing Power ◽  
Cloud Computing Environment ◽  
Proposed Model

AbstractWith the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

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