AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
Sábháilte in:
| Foilsithe in: | Algorithms vol. 18, no. 5 (2025), p. 263 |
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| Príomhchruthaitheoir: | |
| Rannpháirtithe: | , , , , , , , , , |
| Foilsithe / Cruthaithe: |
MDPI AG
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| Ábhair: | |
| Rochtain ar líne: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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MARC
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|---|---|---|---|
| 001 | 3211847023 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1999-4893 | ||
| 024 | 7 | |a 10.3390/a18050263 |2 doi | |
| 035 | |a 3211847023 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231333 |2 nlm | ||
| 100 | 1 | |a Yuan Fujiang |u College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; yuanfujiang@ctbu.edu.cn (F.Y.); | |
| 245 | 1 | |a AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Innovations | ||
| 653 | |a Data processing | ||
| 653 | |a Collaboration | ||
| 653 | |a Classification | ||
| 653 | |a Security | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Privacy | ||
| 653 | |a Blockchain | ||
| 653 | |a Optimization | ||
| 653 | |a Digital currencies | ||
| 653 | |a Transaction processing | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Federated learning | ||
| 653 | |a Pattern recognition | ||
| 653 | |a Internet of Things | ||
| 653 | |a Adaptive algorithms | ||
| 700 | 1 | |a Zuo Zihao |u College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; yuanfujiang@ctbu.edu.cn (F.Y.); | |
| 700 | 1 | |a Jiang, Yang |u College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; yuanfujiang@ctbu.edu.cn (F.Y.); | |
| 700 | 1 | |a Shu Wenzhou |u School of French Studies, Sichuan International Studies University, Chongqing 400031, China | |
| 700 | 1 | |a Tian Zhen |u James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK | |
| 700 | 1 | |a Ye Chenxi |u Faculty of Science and Technology, Hong Kong Baptist University, Hong Kong 999077, China | |
| 700 | 1 | |a Yang, Junye |u College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; yuanfujiang@ctbu.edu.cn (F.Y.); | |
| 700 | 1 | |a Mao Zebing |u Department of Engineering Science and Mechanics, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan | |
| 700 | 1 | |a Huang, Xia |u College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; yuanfujiang@ctbu.edu.cn (F.Y.); | |
| 700 | 1 | |a Gu Shaojie |u Magnesium Research Center, Kumamoto University, Kumamoto 860-8555, Japan | |
| 700 | 1 | |a Peng Yanhong |u College of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China; yuanfujiang@ctbu.edu.cn (F.Y.); | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 5 (2025), p. 263 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211847023/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211847023/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211847023/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |