Blockchain-Empowered Lifecycle Management for AI-Generated Content (AIGC) Products in Edge Networks
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Abstract (arXiv)
The rapid development of Artificial IntelligenceGenerated Content (AIGC) has brought daunting challenges regarding service latency, security, and trustworthiness. Recently, researchers presented the edge AIGC paradigm, effectively optimize the service latency by distributing AIGC services to edge devices. However, AIGC products are still unprotected and vulnerable to tampering and plagiarization. Moreover, as a kind of online non-fungible digital property, the free circulation of AIGC products is hindered by the lack of trustworthiness in open networks. In this article, for the first time, we present a blockchain-empowered framework to manage the lifecycle of edge AIGC products. Specifically, leveraging fraud proof, we first propose a protocol to protect the ownership and copyright of AIGC, called Proof-of-AIGC. Then, we design an incentive mechanism to guarantee the legitimate and timely executions of the funds-AIGC ownership exchanges among anonymous users. Furthermore, we build a multi-weight subjective logic-based reputation scheme, with which AIGC producers can determine which edge service provider is trustworthy and reliable to handle their services. Through numerical results, the superiority of the proposed approach is demonstrated. Last but not least, we discuss important open directions for further research.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Mar 2023.
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Citing papers per year: 2023: 4; 2024: 1; 2025: 1.
6 papers.
- 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
- 2023Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Studycited by 21
- 2023Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyondcited by 14
- 2025Who Owns the Output? Bridging Law and Technology in LLMs Attributioncited by 4
- 2023A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.0cited by 1
- 2024Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implicationscited by 1
- 2025Who Owns the Output? Bridging Law and Technology in LLMs Attribution29 Mar 2025
- 2024Copyleft for Alleviating AIGC Copyright Dilemma: What-if Analysis, Public Perception and Implications19 Feb 2024
- 2023A Survey of Blockchain, Artificial Intelligence, and Edge Computing for Web 3.022 Nov 2023
- 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization10 Aug 2023
- 2023Challenges and Remedies to Privacy and Security in AIGC: Exploring the Potential of Privacy Computing, Blockchain, and Beyond1 Jun 2023
- 2023Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study30 Mar 2023