AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
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...
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| Format: | Article |
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MDPI AG
2025-05-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/5/263 |
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| author | Fujiang Yuan Zihao Zuo Yang Jiang Wenzhou Shu Zhen Tian Chenxi Ye Junye Yang Zebing Mao Xia Huang Shaojie Gu Yanhong Peng |
| author_facet | Fujiang Yuan Zihao Zuo Yang Jiang Wenzhou Shu Zhen Tian Chenxi Ye Junye Yang Zebing Mao Xia Huang Shaojie Gu Yanhong Peng |
| author_sort | Fujiang Yuan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5a141fcb6d0347398a0cec2faec62742 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-5a141fcb6d0347398a0cec2faec627422025-08-20T03:47:52ZengMDPI AGAlgorithms1999-48932025-05-0118526310.3390/a18050263AI-Driven Optimization of Blockchain Scalability, Security, and Privacy ProtectionFujiang Yuan0Zihao Zuo1Yang Jiang2Wenzhou Shu3Zhen Tian4Chenxi Ye5Junye Yang6Zebing Mao7Xia Huang8Shaojie Gu9Yanhong Peng10College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaSchool of French Studies, Sichuan International Studies University, Chongqing 400031, ChinaJames Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UKFaculty of Science and Technology, Hong Kong Baptist University, Hong Kong 999077, ChinaCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaDepartment of Engineering Science and Mechanics, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, JapanCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaMagnesium Research Center, Kumamoto University, Kumamoto 860-8555, JapanCollege of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaWith 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.https://www.mdpi.com/1999-4893/18/5/263AIblockchainconsensussmart contract |
| spellingShingle | Fujiang Yuan Zihao Zuo Yang Jiang Wenzhou Shu Zhen Tian Chenxi Ye Junye Yang Zebing Mao Xia Huang Shaojie Gu Yanhong Peng AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection Algorithms AI blockchain consensus smart contract |
| title | AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection |
| title_full | AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection |
| title_fullStr | AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection |
| title_full_unstemmed | AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection |
| title_short | AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection |
| title_sort | ai driven optimization of blockchain scalability security and privacy protection |
| topic | AI blockchain consensus smart contract |
| url | https://www.mdpi.com/1999-4893/18/5/263 |
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