## 3 Security and Privacy Challenges
### AI Role in EC Security  
Edge intelligence, or edge AI, represents the blending of machine learning (ML) or artificial intelligence (AI) with EC. Edge AI enables both model training and inference directly at the edge through collaboration between edge devices or utilizing local edge servers near the devices [143]. It is significant for adopting self-learning security solutions at the edges, thus fostering the development of adaptive and autonomous security mechanisms capable of addressing emerging threats in real time [144]. AI algorithms can handle highly unpredictable and complex data while ensuring data security against advanced and evolving threats [145]. Edge intelligence implies a network of interconnected systems and devices conceived for data collection, storage, processing, and analysis near the physical location where the data are generated. This methodology aims to enhance the quality and speed of data processing while improving data privacy and security by preserving sensitive information nearer to its source [146]. The convergence of AI and EC is seen as a natural progression due to their clear intersection. EC is centered around coordinating numerous collaborative edge devices and servers, while AI aims to infuse devices with intelligent behavior by learning from data, thereby simulating human-like intelligence. AI is important in ensuring data security through its advanced data processing and pattern recognition capabilities [147]. The taxonomy of AI presents numerous techniques like machine learning (ML), Deep Learning (DL), Natural Language Processing (NLP),  
Figure 8: Example of a Blockchain structure. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Strategy** & **Network Layer** & **Limitations** \\ \hline Cryptographic Schemes & Communication Layer & Power efficiency, computational ability, storage, etc. \\ \hline Secured data aggregation, deduplication, analysis & Data layer & Consumes power, renders sensitive data to intruders’ network bandwidth \\ \hline Combined with Blockchain & Architecture layer & Complex system with more computing capability \\ \hline Intrusion Detection System (IDS) & Communication Layer & Resource consumption \\ \hline \end{tabular}
\end{table} 
Table 5: Strategies against EC security threats [96]. Computer Vision (CV), and Robotics [148]. A Venn diagram shown in Figure 9 demonstrates the relation between artificial intelligence (AI), machine learning, deep learning (DL), data science, and data mining techniques [149]. Machine learning (ML), a subset of AI, learns from past data, whereas deep learning (DL), a more specific area within ML, processes data using several nonlinear transformations. DL, compared to traditional ML methods, has demonstrated a remarkable ability to extract and process data, but it also requires sizable computational resources [150]. Decentralized deep learning (DDL), like federated learning (FL) and swarm learning, is a promising tool in securing the data processing at edge devices [151]. ML algorithms for data security can broadly be categorized into transaction algorithms and decision algorithms. Transaction algorithms handle data exploration and preprocessing tasks, while decision algorithms are used to manage business decisions [152]. A major advantage of DL over traditional ML techniques is its ability to automatically extract complex, high-level features from data. DL uses hierarchical neural network models that automatically learn from unstructured data, such as images, sound, text, and video [35]. [PERSON] et al. have discussed numerous techniques which optimize DL models for EC, such as model pruning, quantization, early exit methods, and approaches in DL tasks distribution between cloud and edge nodes [102]. Data science covers various aspects of data processing, including collection, storage, analysis, cleaning, visualization, interpretation, decision-making, value creation, and effectively reporting relevant insights. Data mining aims to uncover newer, hidden patterns and knowledge from data [153].