## 2 Edge Computing
### Security and Privacy Challenges
#### 2.1.2 Data Security Measures  
Data security and privacy protection techniques require an integrated approach that combines advanced technologies, robust policies, and proactive strategies. A strong encryption mechanism is essential for establishing a protective barrier for data stored at edge nodes and during transmission. Enhanced access controls, multi-factor authentication mechanisms, and regular security assessments help mitigate data privacy and security risks arising from internal threats and potential data manipulation. Advanced anonymization techniques and differential privacy methods enable service providers to preserve client privacy while extracting meaningful insights from raw data. Comprehensive data governance frameworks ensure ethical data handling practices throughout the data lifecycle. Secure data transmission protocols and adherence to regulatory standards help ensure compliance with data protection laws. A brief overview of mitigation strategies against EC data security threats is presented below. * Data anonymization and pseudonymization : Anonymization drops the personally identifiable information (PII) from datasets, thus preventing their association with individuals during data analysis [43]. In contrast, pseudonymization is the processing of personal data so that it cannot be linked to a specific individual without additional information [44]. * Encryption: Data encryption converts data into an unreadable format, accessible only through specific secret information, commonly referred to as a key [45]. Lightweight encryption is ideal for resource-constrained edge devices, while blockchain-based encryption secures decentralized edge networks through immutability. * Secure data aggregation (SDA): SDA optimally compresses data, removes redundancy, and lowers power consumption [46]. In this technique, edge devices encrypt their own data using homomorphic encryption techniques before transmitting it to the EC nodes [40]. * Differential privacy (DP): The DP technique adds random noise to data, obscuring users' sensitive information before it is shared on the network. Unlike traditional privacy protection methods, DP focuses on protecting individual data rather than the entire dataset [47]. * Federated Learning (FL): FL enables the training of deep learning models across multiple participants or computing nodes while keeping data security and privacy \begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \hline
**Type of Threat** & **Description** \\ \hline Privacy leakage & Privacy threats are amplified when legitimate entities, including edge data centers, infrastructure providers, service providers, and end nodes, have access to user data. In an open IoT ecosystem operating across various trust domains, it becomes difficult to authenticate the trustworthiness of service providers [10]. \\ \hline Eavesdropping or sniffing & Adversaries covertly listen to users’ conversations and data transactions, gaining access to user data, passwords, and communication networks [31]. By eavesdropping, they effectively mask themselves within the system environment, making detection difficult [13]. \\ \hline Jamming attacks & Attackers deliberately flood the communication network with falsified data to deplete its communication, computing, and storage resources [31]. In a jamming attack, the network is disrupted and rendered inoperable by the continuous transmission of random data bits or the emission of radio frequency (RF) signals [42]. \\ \hline Integrity attacks against machine learning & Attackers can compromise the training of machine learning models by manipulating or injecting deceptive data into the training datasets. They may also exploit existing vulnerabilities without directly interfering with the training process [31]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._intact. It supports the collaborative training of ML and DL models by leveraging local data on edge devices and sending only the updated model parameters to a central cloud server [48].