Enabling the multi facets of privacy in modern communication systems via cutting-edge techniques and protocols

The rapid advancement of digital communication technologies, accelerated by the conver gence of the Internet of Things (IoT), Industrial IoT (IIoT), and 5G networks, has revolutionized information sharing while introducing new privacy and security challenges in areas such as data privacy, trust management, and secure communications. This work introduces a privacy-centered approach to tackling emerging security challenges, starting with a novel fog-enabled Social Internet of Things (SIoT) architecture that integrates Trust Management Systems (TMS) to enhance service discovery, trustworthiness, and resource efficiency. To protect data at the network edge, the thesis develops a distributed data dissemination framework that utilizes searchable encryption and edge computing to ensure robust data privacy. Additionally, it explores the influence of carrier frequency on the resilience of Radio Frequency Fingerprinting (RFF) within Physical Layer Security (PLS), reinforcing device-level authentication with privacy-preserving elements. Finally, the thesis proposes an end-to-end cryptographic framework for Lawful Interception (LI) in Beyond 5G networks, aiming to strike a balance between stringent privacy protections and compliance with regulatory standards. Together, these contributions offer a multifaceted approach to privacy, blending architectural innovations, encryption mechanisms, and scalable frameworks for the next generation of secure communication networks.