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

TL;DR

The paper surveys how Generative AI (GAI) techniques, notably variational autoencoders, GANs, generative diffusion models, and large language models, can address blockchain challenges in scalability, security, privacy, and interoperability, going beyond traditional discriminative AI. It presents a case study where a Generative Diffusion Model (GDM) optimizes PBFT-based IoT blockchains, achieving faster convergence, higher rewards, and significant throughput and latency gains relative to a conventional DRL baseline. The work demonstrates data-generation, smart-contract design, and network-architecture optimization as concrete GAI-enabled applications, and discusses future directions such as personalized GAI-enabled blockchains and deeper privacy/security considerations. Overall, the findings suggest that integrating GAI into blockchain design can yield practical performance improvements and new capabilities across smart-contract creation, security auditing, and cross-chain optimization, with careful attention to privacy and adversarial risks.

Abstract

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.

Figures (4)

• Figure 1: The schematic of GAI-enabled blockchain. 1) A user generates a public/private key pair to join a blockchain network. GAI can aid in key generation and sharing processes. 2) Once joined, the user can create transactions and smart contracts. GAI can automatically generate smart contracts. 3) Transactions and smart contracts are validated by the consensus mechanism. GAI can audit smart contracts and detect attacks from transactions. GAI can also be leveraged to optimize blockchain network parameters and consensus mechanisms. 4) Once validated, transactions and smart contracts are collected to create a new block to add to the chain. GAI also can generate fake transactions to obfuscate real transactions to improve privacy.
• Figure 2: The model of IoT-orient blockchain system. Note that in PBFT, each node performs two operations for message validation, namely signature validation and message endorsement. The corresponding computation complexity is denoted by $C_S$ and $C_E$, respectively. According to Blockchain2, each node will perform $1$ and $2 + 4(K + f - 1)$ times of signature validation and message endorsement, respectively, where $f$ means the number of malicious block producers. Accordingly, $T^V$ = $\frac{C_S + [2+4(K+f-1)]C_E}{R}$. Since PBFT contains five rounds of broadcast, $T^B$ = $5 \frac{S_B}{R}$. The optimization goal is to maximize $\alpha \cdot \textit{throughput} + \beta \cdot \textit{latency}$, with the constraint that the latency should be less than the user threshold.
• Figure 3: The training curves of GDM and PPO.
• Figure 4: The performance of blockchains designed by diffusion and PPO. Note that the two subfigures show the throughput and latency, whose units are transactions per second and second, respectively. The states 1 and 2 are [12, 30, 15, 34, 15, 8, 5500000, 210, 0.025, 0.01] and [10, 28, 17, 32, 13, 10, 5000000, 200, 0.02, 0.015], respectively.