Security and Privacy of 6G Wireless Communication using Fog Computing and Multi-Access Edge Computing

Abstract

The challenges surrounding the confidentiality of data transmission in the context of the upcoming sixth-generation (6G) wireless networks are proposed in this research. The study explores the potential role of blockchain systems in enhancing data security. It examines the integration of machine learning (ML) techniques to address the growing complexities of handling massive data volumes within the 6G environment. This research involves a comprehensive survey of existing strategies for maintaining data confidentiality in automotive communication systems. It further investigates an analysis of confidentiality approaches inspired by the 6G network architecture. The study examines the potential security implications of the Internet of Everything (IoE). It evaluates current research issues related to safeguarding data confidentiality within the framework of 6G communication among vehicles. The exploration involves reviewing ML techniques and their applicability in resolving the data processing challenges inherent in the 6G wireless network environment. The proposed work reveals the increasing complexity and variability of the 6G wireless network environment, leading to potential challenges in protecting private and confidential data during communication. It highlights the promising role of blockchain systems in addressing data security concerns within the 6G network context. Additionally, the study underscores the transformative potential of integrating ML techniques to handle the massive data volumes generated within the 6G ecosystem. The research highlights the importance of these technologies in mitigating data security risks and ensuring the confidentiality of information exchanged within the 6G communication framework.