Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study

• Published in: IEEE network p. 1
• Main Authors: Nguyen, Cong T., Liu, Yinqiu, Du, Hongyang, Hoang, Dinh Thai, Niyato, Dusit, Nguyen, Diep N., Mao, Shiwen
• Format: Journal Article
• Language: English
• Published: IEEE 10.06.2024
• Subjects: Blockchain; Blockchains; Consensus protocol; Data privacy; Generative Adversarial Network; Generative Artificial Intelligence; Generative Diffusion Model; Large Language Model; Privacy; Scalability; Security; Smart contracts; Variational Autoencoder
• ISSN: 0890-8044; 1558-156X
• DOI: 10.1109/MNET.2024.3412161

Generative Artificial Intelligence (GAI) has recently emerged as a promising solution to address critical challenges of blockchain technology, including scalability, security, privacy, and interoperability. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains. Then, we discuss emerging solutions that demonstrate the effectiveness of GAI in addressing various challenges of blockchain, such as detecting unknown blockchain attacks and smart contract vulnerabilities, designing key secret sharing schemes, and enhancing privacy. Moreover, we present a case study to demonstrate that GAI, specifically the generative diffusion model, can be employed to optimize blockchain network performance metrics. Experimental results clearly show that, compared to a baseline traditional AI approach, the proposed generative diffusion model approach can converge faster, achieve higher rewards, and significantly improve the throughput and latency of the blockchain network. Additionally, we highlight future research directions for GAI in blockchain applications, including personalized GAI-enabled blockchains, GAI-blockchain synergy, and privacy and security considerations within blockchain ecosystems.