# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 3 AI-Driven Blockchain Solutions
### AI-Driven Consensus Algorithm
#### 3.1.2 AI-Driven Security and Privacy-Preserving Consensus  
Security and privacy protection are fundamental concerns in blockchain systems as they ensure the integrity, confidentiality, and trustworthiness of distributed data. In various applications, such as financial transactions, healthcare, and decentralized identity management, safeguarding sensitive information against cyber threats, unauthorized access, and data breaches is crucial. To address these challenges, AI-driven consensus mechanisms for security and privacy protection have emerged as a promising solution. By integrating AI with blockchain consensus, these mechanisms enhance anomaly detection, prevent malicious node behavior, and improve data protection through techniques such as differential privacy, federated learning, and deep-learning-based intrusion detection. [PERSON] et al. [120] proposed an architecture in which devices (also referred to as nodes) can reach consensus on task results using off-chain smart contracts and private data. The proposed distributed computing architecture accelerates compute-intensive and data-intensive supervised classification algorithms, even in resource-constrained environments. This architecture significantly enhances privacy protection, preventing the leakage of distributed data. [PERSON] et al. [121] aimed to design and implement a consensus mechanism that combines blockchain and AI to enable successful big data analytics. This work presents an improved IIoT network based on the DPoS algorithm, which integrates blockchain and AI for real-time data transmission. To accelerate block generation in the IIoT network, nodes use the improved DPoS to reach consensus, select representatives, and store block information in transaction nodes. [PERSON] et al. [122] proposed a blockchain-supported eXplainable AI (XAI) to enhance decision-making capabilities in network threat detection within intelligent healthcare systems. Specifically, the proposed method first implements the Clique Proof of Authority (C-PoA) consensus to verify and store data across multiple cloud providers using blockchain. Secondly, a novel deep-learning-based \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Algorithm** & **Core Mechanism** & \begin{tabular}{c} **Communication** \\ **Complexity** \\ \end{tabular} & **Key Advantages** & **Existing Limitations** \\ \hline PoAh 2.0 [116] & \begin{tabular}{c} AI-powered \\ dynamic \\ authentication \\ \end{tabular} & \(O(N)\) & \begin{tabular}{c} Suitable for IoT \\ devices, enhanced \\ security \\ \end{tabular} & \begin{tabular}{c} Dependence on AI \\ computational \\ capacity \\ \end{tabular} \\ \hline IoV Consensus [117] & \begin{tabular}{c} Vehicle prediction + \\ BP neural network \\ \end{tabular} & \(O(N\log N)\) & \begin{tabular}{c} Reduces leader node \\ dropouts \\ \end{tabular} & \begin{tabular}{c} Limited accuracy in \\ vehicle time \\ prediction \\ \end{tabular} \\ \hline FGADL-DEVCA [118] & \begin{tabular}{c} Deep learning for \\ transaction \\ verification \\ \end{tabular} & \(O(N^{2})\) & \begin{tabular}{c} Improves data \\ integrity \\ \end{tabular} & \begin{tabular}{c} High computational \\ cost \\ \end{tabular} \\ \hline BDEV-CAM [119] & \
\begin{tabular}{c} Machine-learning- \\ enhanced blockchain \\ verification \\ \end{tabular} & \(O(N\log N)\) & Enhance IoT security &
\begin{tabular}{c} High computational \\ demands \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of AI-powered authentication and IoT consensus optimization algorithms. threat tracking model is constructed by combining parallel stacked long short-term memory (PSLSTM) networks with multi-head attention mechanisms to improve attack detection. Extensive experiments confirm that it has the potential to be used by cybersecurity analysts as an enhanced decision support system. [PERSON] et al.