Analysis of the Architecture of the Mental Health Education System for College Students Based on the Internet of Things and Privacy Security

• Main Authors: Ruijian Xiao, Xingeng Liu
• Format: Article
• Language: English
• Published: IEEE 2021-01-01
• Series: IEEE Access
• Subjects: Internet of Things; privacy security; mental health; differential privacy; college student education; extreme learning machine
• Online Access: https://ieeexplore.ieee.org/document/9442707/

In recent years, the rapid development of computer and network technology has produced various positive and negative effects on the mental health of college students. This also brings challenges to mental health education in colleges. In order to strengthen the research on the mental health education model under the network environment, this paper proposes the architecture of the college student mental health education system based on the privacy and security of the Internet of Things. First of all, this article combines the 3DES-RC4 hybrid security encryption algorithm based on the Internet of Things. This article uses the C/S architecture, MQTT protocol and SIP protocol based on the Internet of Things structure to design and implement instant messaging IoT security for mental health education Architecture. The extreme learning machine method combined with the differential privacy method is used in this article. By adding noise to the query results and adding an appropriate amount of noise to the analysis results, the protection of private data can be achieved. Finally, the data set experiment proves that compared with the existing algorithms, the algorithm and model proposed in this paper can better balance the level of privacy protection and classification accuracy.