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Algorithms
Volume 18
Issue 5
10.3390/a18050263
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection

by
Fujiang Yuan
1,
Zihao Zuo
1,
Yang Jiang
1,
Wenzhou Shu
2,
Zhen Tian
3,
Chenxi Ye
4,
Junye Yang
1,
Zebing Mao
5,
Xia Huang
1,
Shaojie Gu
6 and
Yanhong Peng
1,*
1
College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China
2
School of French Studies, Sichuan International Studies University, Chongqing 400031, China
3
James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
4
Faculty of Science and Technology, Hong Kong Baptist University, HongKong 999077, China
5
Department of Engineering Science and Mechanics, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan
6
Magnesium Research Center, Kumamoto University, Kumamoto 860-8555, Japan
*
Author to whom correspondence should be addressed.
Algorithms 2025, 18(5), 263; https://doi.org/10.3390/a18050263
Submission received: 1 April 2025 / Revised: 28 April 2025 / Accepted: 29 April 2025 / Published: 2 May 2025
(This article belongs to the Special Issue Blockchain and Big Data Analytics: AI-Driven Data Science)
Versions Notes

Abstract

With the continuous development of technology, blockchain has been widely used in various fields by virtue of its decentralization, data integrity, traceability, and anonymity. However, blockchain still faces many challenges, such as scalability and security issues. Artificial intelligence, with its powerful data processing capability, pattern recognition ability, and adaptive optimization algorithms, can improve the transaction processing efficiency of blockchain, enhance the security mechanism, and optimize the privacy protection strategy, thus effectively alleviating the limitations of blockchain in terms of scalability and security. Most of the existing related reviews explore the application of AI in blockchain as a whole but lack in-depth classification and discussion on how AI can empower the core aspects of blockchain. This paper explores the application of artificial intelligence technologies in addressing core challenges of blockchain systems, specifically in terms of scalability, security, and privacy protection. Instead of claiming a deep theoretical integration, we focus on how AI methods, such as machine learning and deep learning, have been effectively adopted to optimize blockchain consensus algorithms, improve smart contract vulnerability detection, and enhance privacy-preserving mechanisms like federated learning and differential privacy. Through comprehensive classification and discussion, this paper provides a structured overview of the current research landscape and identifies potential directions for further technical collaboration between AI and blockchain technologies.
Keywords: AI; blockchain; consensus; smart contract AI; blockchain; consensus; smart contract

Share and Cite

MDPI and ACS Style

Yuan, F.; Zuo, Z.; Jiang, Y.; Shu, W.; Tian, Z.; Ye, C.; Yang, J.; Mao, Z.; Huang, X.; Gu, S.; et al. AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection. Algorithms 2025, 18, 263. https://doi.org/10.3390/a18050263

AMA Style

Yuan F, Zuo Z, Jiang Y, Shu W, Tian Z, Ye C, Yang J, Mao Z, Huang X, Gu S, et al. AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection. Algorithms. 2025; 18(5):263. https://doi.org/10.3390/a18050263

Chicago/Turabian Style

Yuan, Fujiang, Zihao Zuo, Yang Jiang, Wenzhou Shu, Zhen Tian, Chenxi Ye, Junye Yang, Zebing Mao, Xia Huang, Shaojie Gu, and et al. 2025. "AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection" Algorithms 18, no. 5: 263. https://doi.org/10.3390/a18050263

APA Style

Yuan, F., Zuo, Z., Jiang, Y., Shu, W., Tian, Z., Ye, C., Yang, J., Mao, Z., Huang, X., Gu, S., & Peng, Y. (2025). AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection. Algorithms, 18(5), 263. https://doi.org/10.3390/a18050263

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