## 5 The Security and Privacy of Edge Computing  
EC has become a critical technology for enhancing privacy, security, and resilience against cyber-attacks in smart grids. Additionally, it addresses key challenges such as the efficient deployment of IoT devices and the management of processing loads. Several studies have made significant contributions by focusing on security and privacy requirements. Ref. [219] examines the cybersecurity challenges and solutions for cyber-physical systems (CPS) within the IIoT-edge computing context, highlighting common threats such as DoS and ransomware. It also discusses various security technologies, including machine learning, blockchain, and encryption, as essential tools for improving security and real-time data protection in IIoT-edge environments, while outlining future research directions in this area. Refs. [50,220] analyzed cybersecurity vulnerabilities in smart grids, including power outages, protocol failures, and breaches, and proposed solutions to enhance resilience using secure communication and intrusion detection technologies. Additionally, security and privacy concerns in EC-supported IoT systems have been thoroughly explored [50], with an analysis of threat vectors and vulnerabilities by ETSI standards. Solutions against these threats and future roadmaps have also been proposed [221]. Infrastructure, security, privacy, and scalability challenges are discussed in detail, examining the integration of edge computing and IoT technologies in smart cities through applications such as energy management, public safety, healthcare, and transportation [222]. Ref. [223] evaluates decentralized edge architectures, edge nodes, and gateways, examining their role in various Industry 5.0 applications such as smart cities, autonomous systems, and healthcare. EC introduces security and privacy challenges alongside deploying heterogeneous devices and the complexities of big data processing. Ref. [224] addressed security and privacy issues in EC architecture, discussing the role of ML and DL algorithms in combating these threats, especially to reduce the risks associated with a large attack surface. For IoT systems, ref. [225] proposed FC and EC approaches to facilitate the handling of confidential user data and improve data security. This study proposes enhancing security by continuously monitoring system resources and restricting data transmission to processed information. Additionally, a System Security Manager is introduced to mitigate vulnerabilities associated with device-level attacks. As these advancements in security and privacy through EC are crucial, they enable the development of more resilient and secure Digital Twins (DT) for grid systems, especially \begin{table}
\begin{tabular}{c c c} \hline
**Ref. No** & **Scenarios** & **Key Technologies** \\ \hline
[187] & Smart Metering System & DL and EC \\
[189] & Smart Metering System & Knowledge-based Usage Strategy for Smart Meters \\
[191] & Smart Metering System & EC in Long Range (LoRa) and DL Based \\
[193] & Anomaly Detection & DT to Filter the Abnormal Data \\
[195] & Anomaly Detection & Polynomial Regression and Gaussian Distribution \\
[196] & Anomaly Detection & KDDCUP99 Datasets \\
[202] & State Estimation & FL Framework for False Data Injection \\
[204] & State Estimation & Data-Driven Algorithm \\
[205] & State Estimation & Genetic Algorithm III (NSGA-III) \\
[208] & Energy Management System & Privacy-Preserving Average Consensus Algorithm \\
[209] & Energy Management System & FL and DRL Algorithms \\
[210] & Energy Management System & Reinforcement Learning Algorithm \\
[211] & Energy Management System & Stackelberg and Lyapunov Algorithms \\
[212] & Fault Location & TWAM \\
[218] & Fault Location & Power Signal Fault Signal Analysis and EC \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._in predictive maintenance and resilience applications [226]. This makes it an essential component for developing DT in grid resilience and predictive maintenance systems. In line with this, ref.