Deep-Learning-Based Mobile Group Intelligence Perception Mechanism Oriented to User Privacy and Data Security in the Internet of Things

【Author】 Hu, Hexuan; Wang, Lizhi; Hu, Qiang; Bu, Yongxi; Zhang, Ye

【Source】IEEE WIRELESS COMMUNICATIONS

【影响因子】12.777

【Abstract】With the rapid development of the Internet of Things, a large number of mobile devices participate in the perception and aggregation of data, outsourcing and storing massive data on various cloud platforms. Thus, a new data perception and privacy protection model based on mobile group intelligence perception and cloud computing is required. The existing work on data security and privacy protection mainly focuses on the independent link of data collection, aggregation, and services, which lacks holistic consideration for the different service requirements. To end this issue, this article comprehensively integrates the security and privacy protection requirements of various stages in the mobile group intelligence perception for data collection, aggregation, and service from a global perspective. Aiming at data privacy and security issues in the existing mobile group intelligence perception system, and the difficulty in guaranteeing the quantity and quality of data at the same time, a mobile group intelligence perception mechanism oriented to user privacy and data security is proposed. This mechanism uses deep learning as its core algorithm to handle big data cases. It can provide authenticity and reliability guarantees for the subsequent data application on the premise of protecting user privacy. Experimental results prove that the mechanism proposed in this article meets the security requirements, and its user-end computing overhead is small.

【Keywords】Deep learning; Data privacy; Cloud computing; Data security; Smart contracts; Blockchains; Internet of Things

【发表时间】2022 APR

【收录时间】2022-07-10

【文献类型】理论性文章

【主题类别】

区块链技术-协同技术-物联网