## 3 Security and Privacy Challenges
### Mitigation Strategies Against EC Security Challenges  
The countermeasures against security and privacy challenges in an EC-driven IoT network are discussed in numerous works of literature and can be summarized and placed under classes as shown in Table 5.  
* _Cryptographic schemes_: The edge layer, which includes local data centers, as well as sensing devices, is vulnerable to security threats. These edge devices need cybersecurity solutions within limited storage and computation capabilities. A Zero-Trust approach is recommended for securing data in the EC paradigm, with an assumption that all devices have been compromised and all access has to be strictly monitored and authenticated. The standard encryption/decryption methods are memory- and computing-exhaustive [128]. ISO/IEC 29192, Lightweight cryptography is a cryptographic algorithm meant for implementation in constrained environments, including RFID tags, sensors, contactless smart cards, healthcare devices, etc., for the protection of communication protocols. * _Secured data aggregation, deduplication, analysis_: Data aggregation is a method of clustering the data from various edge nodes by reducing the number of transfers and hence eliminating redundancy. Secure Data Aggregation (SDA) is a highly secure, privacy-preserving, and efficient data compression technique using homomorphic encryption against security attacks such as eavesdropping and forging. The secure deduplication technique removes matching copies of data while supporting data security. It employs Convergent Encryption (CE) for encrypting or decrypting data at the file level, along with a convergent key [129]. Load distribution is used in EC for even distribution of computational, network traffic, data storage, and security-related tasks across edge devices, edge servers, and the cloud. Thus, load distribution prevents edge devices or servers from becoming overwhelmed by diverse tasks while ensuring key security measures like encryption, intrusion detection, and access control are in place. [PERSON] et al. estimated an optimal number of edge nodes that can be assigned to a particular edge server using Equation (1) and further used it in estimating its security factor [130]. \[\Delta_{2}=\sum_{i=1}^{n}\frac{\omega_{i}\times\sum_{\varphi=1}^{\delta}\phi_{ \varphi}\frac{K_{\varphi}-K_{\varphi_{\min}}}{K_{\varphi_{\max}}-K_{\varphi_{ \min}}}}{\sum_{i=1}^{n}\omega_{j}}\] (1) \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline
**Strategy** & **Description** \\ \hline Proper Encryption & A complicated algorithm or a secure password exchanged exclusively between legitimate senders and recipients, granting access solely to genuine users. \\ \hline Intrusion Detection System & Identifies any abnormality or illegal access and alerts the user in case of dubious activities. \\ \hline User Behavior Profiling & Maintaining a record of users’ behavior and keeping track of activities apart from normal behavior to detect an attacker’s presence. \\ \hline Cryptographic Techniques & Secures significant data using codes that block security attacks through a secret key. \\ \hline Data Confidentiality & Mitigates privacy concerns while restricting unauthorized data transactions, data loss, data tampering, data breaches, and related issues. \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._\(\omega_{i}(i=1,\ldots,n)\) represents the number of edge devices associated with a particular edge server. Thus, the percentage of devices assigned to edge server \(i\) is found by dividing \(\omega_{i}\) by the total number of devices \(\left(\sum_{j=1}^{n}\omega_{j}\right)\). (\(\varphi=1,\ldots,\delta\)) is the min-max normalized security Key Performance Indicator (KPI) while \(\varphi\) regulates priority metrics. * _Combined with blockchain_: The advantage of implementing blockchain with EC is that it can offer secure data transfer and processing without needing a centralized server by deploying distributed ledger technology.