Holistic big data integrated artificial intelligent modeling to improve privacy and security in data management of smart cities

Jie Chen, L. Ramanathan, Mamoun Alazab

Research output: Contribution to journalArticlepeer-review

Abstract

The smart city adopts information and communication technology (ICT), contributing to the growth, implementation, and advancement of sustainable development practices to address growing challenges to urbanization. The demanding factor in smart city applications is privacy, security, confidentiality, and authenticity, which are considered a prominent factor in smart city infrastructure's data management interface. Hence In this paper, to resolve such issues, Holistic Big Data Integrated Artificial Intelligent Modeling (HBDIAIM) has been proposed to improve the privacy and security aspects of data management interface in various smart city applications. In HBDIAIM, a differential evolutionary algorithm has been incorporated to build adequate security for the confidential data management interface in smart city applications. Furthermore, the Big Data analytics assisted decision privacy scheme has been used in the differential evolutionary algorithm, which improves the scalability and accessibility of the information in a data management interface based on their corresponding storage location. In addition, the Adaptable interference method is designed and developed to optimize the scalability and privacy issues data management interface of various smart city applications. The simulation analysis is performed based on security, accuracy, performance, and scalability proves the reliability of the proposed framework.

Original languageEnglish
Article number103722
Pages (from-to)1-10
Number of pages10
JournalMicroprocessors and Microsystems
Volume81
DOIs
Publication statusPublished - Mar 2021

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