Blockchain Papers

Christopher P. Albertyn, Svitlana Surodina, Bo Tan, Tina Woods · 7 authors

UNSTRUCTURED Global healthcare systems need to evolve to ensure optimal, safe, and ethical utilisation of health data and the latest digital technologies, such as Artificial Intelligence (AI), Privacy Enhancing Technologies (PETs), and Distributed Ledger Technologies (DLTs), to meet the challenges of a global ageing population. Simultaneously, the increasing capabilities of remote measurement technologies and the proliferation of 5G networks demonstrates that digital technologies are now more accessible to a much larger population, offering an opportunity for decentralised democratised health data use that supports individual agency. Given the significant international human and economic cost of cognitive decline and dementia, we propose that a person-centred decentralised health data ecosystem, underpinned by these emerging technologies and opportunities, would reduce burden on cognitive healthcare systems by intervening earlier, accelerate clinical research innovation in dementia, and extend cognitive healthspan. Crucially, we argue for the importance of including the individual, as well as other key stakeholders, in the development, continuing operation, and as a shared beneficiary of any potential accrued value emerging from this ecosystem.

Xueping Liang, Juan Zhao, Yan Chen, Eranga Bandara · 5 authors

BACKGROUND: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. OBJECTIVE: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. METHODS: We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. RESULTS: We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. CONCLUSIONS: Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne

No abstract is available for this record.

Hakima Rym Rahal, Sihem Slatnia, Okba Kazar, Ezedin Barka · 5 authors

No abstract is available for this record.

Rabimba Karanjai, Edward Li, Lei Xu, Weidong Shi

The introduction of large language models (LLMs) like ChatGPT and Google Palm2 for smart contract generation seems to be the first well-established instance of an AI pair programmer. LLMs have access to a large number of open-source smart contracts, enabling them to utilize more extensive code in Solidity than other code generation tools. Although the initial and informal assessments of LLMs for smart contract generation are promising, a systematic evaluation is needed to explore the limits and benefits of these models. The main objective of this study is to assess the quality of generated code provided by LLMs for smart contracts. We also aim to evaluate the impact of the quality and variety of input parameters fed to LLMs. To achieve this aim, we created an experimental setup for evaluating the generated code in terms of validity, correctness, and efficiency. Our study finds crucial evidence of security bugs getting introduced in the generated smart contracts as well as the overall quality and correctness of the code getting impacted. However, we also identified the areas where it can be improved. The paper also proposes several potential research directions to improve the process, quality and safety of generated smart contract codes.

Yi Yang, Jiaxuan Wei, Zhixuan Yu, Ruisheng Zhang

No abstract is available for this record.

Badal Gami, Manav Agrawal, Deepak Mishra, Danish Quasim · 5 authors

Abstract While blockchain technology (BT) is considered secure, there are several vulnerabilities that can breach its security. The study in artificial intelligence (AI) and BT is widely popular due to its expanding importance in enhancing security and computational prowess. In this study, we present a comprehensive and meticulous comprehensive review of AI and BT‐based privacy‐preserving smart healthcare. The selection for this study was based on a holistic and integrated approach which involved examining not only individual studies but also their relationships, and trends. Through a systematic analysis of various phases, we identified 91 primary studies pertaining to information needed to stockpile directions called for retorting the research queries. We have undertaken a descriptive comparison of foundational manuscripts, taking into account an array of essential factors, including performance metrics, security protocols, and computational prowess. Our thorough discussions and debates have led to the identification of research gaps in the current manuscript, as well as the direction for future research. We also propose our constructive approach for the aforementioned integration, highlighting its potential benefits and implications.

黃紹倫, HUANG, HSAO-LUN

碩士

Neelesh Mungoli

This paper explores the potential of using ChatGPT, a state-of-the-art conversational AI system, to enhance engagement and understanding of cryptocurrency. We first provide a comprehensive review of the existing literature on both cryptocurrency and ChatGPT. We then describe the background of cryptocurrency and the capabilities of ChatGPT. We present our methodology for collecting and preprocessing a dataset of cryptocurrency-related conversations and fine-tuning ChatGPT using reinforcement learning. Our results demonstrate the effectiveness of ChatGPT in generating contextually appropriate responses to cryptocurrency-related queries, with potential applications in areas such as customer support and education. However, we also identify challenges and limitations associated with the deployment of ChatGPT in this domain, including the need for robust data privacy measures and addressing potential biases. Our findings suggest promising directions for future research in enhancing conversational engagement and understanding of cryptocurrency through ChatGPT

Junyu Ren, Tuanfa Qin

Although smart healthcare has achieved rapid development and has gained considerable attention from academia and industry, pressing challenges exist, such as resource limitation and stringent security demand. To tackle the issues, a blockchain-based and trust-aware task-offloading strategy (BBTAS) is proposed in this work, whereby task-offloading requests are published as blockchain transactions that are automatically yet securely performed by smart contracts (SCs). An SC is a set of codes implementing predefined rules agreed by the participants involved. When a rule meets certain criteria or triggered by some events or transactions, an SC can perform by itself and generate verifiable results, which can be validated and securely stored in the blockchain. The proposed recommendation filtering method (RFM) and trust penalty measure (TPM)-based trust mechanism is powerful in resisting network insider attacks, and the inherent time-inefficiency problem of blockchain can be alleviated by integrating trust factor, which is theoretically analyzed in this work. To optimize the selection of service node, a utility-based decision-making method (UB-DMM) is proposed, which can maximize the system utility on the basis of fully considering multiple significant performance metrics of the system. Extensive simulations have been carried out to validate the effectiveness and superiority of the proposed measures.

A. Ezil Sam Leni, Rajendran Shankar, R. Thiagarajan, Vishal Ratansing Patil

The medical sector actively changes and implements innovative features in response to technical development and revolutions.Many of the most crucial elements in IoT-connected health services are safeguarding critical patient records from prospective attackers.As a result, BlockChain (BC) is gaining traction in the business sector owing to its large implementations.As a result, BC can efficiently handle everyday life activities as a distributed and decentralized technology.Compared to other industries, the medical sector is one of the most prominent areas where the BC network might be valuable.It generates a wide range of possibilities and probabilities in existing medical institutions.So, throughout this study, we address BC technology's widespread application and influence in modern medical systems, focusing on the critical requirements for such systems, such as trustworthiness, security, and safety.Furthermore, we built the shared ledger for blockchain-based healthcare providers for patient information, contractual between several other parties.The study's findings demonstrate the usefulness of BC technology in IoHT for keeping patient health data.The BDSA-IoHT eliminates 2.01 seconds of service delay and 1.9 seconds of processing time, enhancing efficiency by nearly 30%.

Zahra Mohammadzadeh, Marcel Ausloos, Hamid Reza Saeidnia

Purpose ChatGPT from OpenAI is an amazing example of machine learning technology. This technology has now become an important issue for high-tech plagiarism concern. Indeed, there are many concerns about using this tool, perhaps using other technologies to make ChatGPT safer. Non-fungible tokens (NFTs) may be a way out. This paper aims to discuss such an alternative. Design/methodology/approach To preventing with high-tech plagiarism created by the ChatGPT tool two ways can help schools, universities and scientific centers to prevent academic plagiarism: first, by banning ChatGPT and adjusting teaching styles, and second, by using detecting AI-produced content. In this viewpoint, the authors suggest a third way that can be a way out. Findings NFTs technology has the ability to add a non-fungibility feature to any digital object (image, text or video). Therefore, any text produced by artificial intelligence tools can be given a specific NFT code. With this work, the authors add a feature to texts produced by artificial intelligence, that is, the non-fungibility feature. Originality/value In this viewpoint, how and why NFTs may be a usefully added value in preventing acts of high-tech plagiarism on ChatGPT is discussed.

C. U. Om Kumar, Sudhakaran Gajendran, Viswaksena Balaji, A. Nhaveen · 5 authors

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Zongwei Li, Dechao Kong, Yuanzheng Niu, Hong-Li Peng · 6 authors

With the widespread attention and application of artificial intelligence (AI) and blockchain technologies, privacy protection techniques arising from their integration are of notable significance. In addition to protecting privacy of individuals, these techniques also guarantee security and dependability of data. This paper initially presents an overview of AI and blockchain, summarizing their combination along with derived privacy protection technologies. It then explores specific application scenarios in data encryption, de-identification, multi-tier distributed ledgers, and k-anonymity methods. Moreover, the paper evaluates five critical aspects of AI-blockchain-integration privacy protection systems, including authorization management, access control, data protection, network security, and scalability. Furthermore, it analyzes the deficiencies and their actual cause, offering corresponding suggestions. This research also classifies and summarizes privacy protection techniques based on AI-blockchain application scenarios and technical schemes. In conclusion, this paper outlines the future directions of privacy protection technologies emerging from AI and blockchain integration, including enhancing efficiency and security to achieve a more comprehensive privacy protection of privacy.

Madhuri Hiwale, Rahee Walambe, Vidyasagar Potdar, Ketan Kotecha

The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.

Genqing Bian, Wenjing Qu, Bilin Shao

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.

Abeda Begum Mahammad, Rajeev Kumar

The technological advent of Blockchain, Bigdata, Cloud, and AI made it easier to be stored and retrieved Electronic Health Record (EHR) data and then used it by patients, doctors, and research groups for treatments and analytics. Blockchain security ensures the security of patients' records data with smart contracts due to the most reliable and secure platform for many industries such as finance, IoT, healthcare, and logistics management. The innovative use of smart contracts in the healthcare industry enables a secure system to provide privacy and complete protection with patients' record data and to be able to integrate with various entities in the healthcare sector. The criticality of acquiring, storing, and securing EHR is essential in the healthcare industry to provide faster services. Integrating EHR data with multiple healthcare providers accrues various benefits such as quick insurance approvals and dealing with emergencies and critical situations. It expedites medical records access to patients, doctors, insurance companies, primary healthcare providers, pharmaceuticals, and laboratories to reap healthcare benefits efficiently. The challenges with EHR data are security and storage, which can be addressed with Blockchain security, and integrating it with Bigdata provides huge benefits of data storage, scalability, and performance to store and maintain data efficiently. Often acquiring, storing, securing, and maintaining healthcare data involves excessive administrative and operative costs. Most of the research work is done on Blockchain technology frameworks and implementation but the most critical part of data storage and maintenance is yet to be focused area, as how to manage this data in the long term. This paper intends to research the best Blockchain based architectural framework to store EHR data in Bigdata storage systems at a lesser cost compared to other Infrastructure facilities by utilizing inexpensive hardware grouped into clusters and assigned resources to share across the network and easy to maintain even after procuring the data across the entities securely.

K Shruthi, A.S. Poornima

In the present medical services, the board, clinical well-being records are as electronic clinical record (EHR/EMR) frameworks.These frameworks store patients' clinical histories in a computerized design.Notwithstanding, a patient's clinical information is gained in a productive and ideal way and is demonstrated to be troublesome through these records.Powerlessness constantly prevents the well-being of the board from getting data, less use of data obtained, unmanageable protection controls, and unfortunate information resource security.In this paper, we present an effective and safe clinical information resource, the executives' framework involving Blockchain, to determine these issues.Blockchain innovation facilitates the openness of all such records by keeping a block for each patient.This paper proposes an engineering utilizing an off-chain arrangement that will empower specialists and patients to get records in a protected manner.Blockchain makes clinical records permanent and scrambles them for information honesty.Clients can notice their wwell-being records, yet just patients own the confidential key and can impart it to those they want.Smart contracts likewise help our information proprietors to deal with their information access in a permission way.The eventual outcome will be seen as a web and portable connection point to get to, identify, and guarantee high-security information handily.In this adventure, we will give deals with any consequences regarding the issues associated with clinical consideration data and the chiefs using AI and Blockchain.Removing only the imperative information from the data is possible with the use of AI.This is done using arranged estimations.At the point when this data is taken care of, the accompanying issue is information sharing and its constancy.This is where Blockchain comes into the picture.Understanding Blockchain development guarantees that data is real and trades are secure.Blockchain development could work on clinical benefits by setting patients at the point of convergence of the clinical consideration structure and extending the insurance and interoperability of prosperity data.This paper is based in a general sense on dealing with clinical benefits data the board issues using Blockchain development and including a couple of key AI components.The fundamental thought process is to bring the attributes of AI and Blockchain together.AI assumes a pivotal part in identifying lethal illnesses.Then again, Blockchain innovation can reform clinical information base interoperability and limit unapproved record admittance.This would guarantee that the touchy patient information is firmly gotten.Expects to construct a safe, ML-driven medical care executive's framework that would guarantee that the sicknesses are precisely anticipated and sorted in the beginning phase.Further, it guarantees that the prepared model channels the information and disposes of the multitude of individual subtleties of the patient and safeguards it from information holes and breaks.It drives the framework with Blockchain to get the exchanges among patients and the approved specialist.It also gives patients the adaptability to pick which specialist should see their wwell-being record and who should not.

Omid Moztarzadeh, Mohammad Jamshidi, Saleh Sargolzaei, Fatemeh Keikhaee · 7 authors

Advanced mathematical and deep learning (DL) algorithms have recently played a crucial role in diagnosing medical parameters and diseases. One of these areas that need to be more focused on is dentistry. This is why creating digital twins of dental issues in the metaverse is a practical and effective technique to benefit from the immersive characteristics of this technology and adapt the real world of dentistry to the virtual world. These technologies can create virtual facilities and environments for patients, physicians, and researchers to access a variety of medical services. Experiencing an immersive interaction between doctors and patients can be another considerable advantage of these technologies, which can dramatically improve the efficiency of the healthcare system. In addition, offering these amenities through a blockchain system enhances reliability, safety, openness, and the ability to trace data exchange. It also brings about cost savings through improved efficiencies. In this paper, a digital twin of cervical vertebral maturation (CVM), which is a critical factor in a wide range of dental surgery, within a blockchain-based metaverse platform is designed and implemented. A DL method has been used to create an automated diagnosis process for the upcoming CVM images in the proposed platform. This method includes MobileNetV2, a mobile architecture that improves the performance of mobile models in multiple tasks and benchmarks. The proposed technique of digital twinning is simple, fast, and suitable for physicians and medical specialists, as well as for adapting to the Internet of Medical Things (IoMT) due to its low latency and computing costs. One of the important contributions of the current study is to use of DL-based computer vision as a real-time measurement method so that the proposed digital twin does not require additional sensors. Furthermore, a comprehensive conceptual framework for creating digital twins of CVM based on MobileNetV2 within a blockchain ecosystem has been designed and implemented, showing the applicability and suitability of the introduced approach. The high performance of the proposed model on a collected small dataset demonstrates that low-cost deep learning can be used for diagnosis, anomaly detection, better design, and many more applications of the upcoming digital representations. In addition, this study shows how digital twins can be performed and developed for dental issues with the lowest hardware infrastructures, reducing the costs of diagnosis and treatment for patients.

Veerasathpurush Allareddy, Sankeerth Rampa, Shankar Rengasamy Venugopalan, Mohammed H. Elnagar · 7 authors

There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.

Authors unavailable

Health Insurance Portability and Accountability Act Regulations place a high priority on healthcare data security. In 2021, there were over 750 data breaches, and the top seven of those exposed over 193 million personal records to fraud and identity theft. Data security is the process of preventing data from being accessed by unauthorised parties and being corrupted at any point in its lifespan. Data across all apps and platforms is protected via data encryption, hashing, tokenization, and key management procedures. The security solution now in use data encryption software to successfully improve data security by converting plain text into encrypted cypher text using an algorithm (referred to as a cypher) and an encryption key. The encrypted data will be unintelligible to unauthorised individuals. With a permitted key, only that user can then decrypt the data. Yet, when data security becomes more lax, confidential data is lost because the key is so easily hackable due to the use of a single algorithm. This project offers a solution for this issue: an effective data security system that heavily relies on WEB 3.0 and smart contracts to protect data. Additionally, it offers total data protection, ensuring that a hacker is unable to alter the data in any way. The development of a framework known as WEB 3.0 includes a block chain framework to safeguard the data at the backend. Data access does not require an encryption or decryption key, thus there is no need to worry about data breaches or tampering by hackers. Thus, this system offers complete data protection for a hospital's medical records.

R. Sreejith, S. Senthil

The exponential growth in the global population and significant advancements in healthcare broadened the scope of intervention for e-Healthcare through decentralized data access and information exchange, making complex clinical decisions. e-Healthcare can perform several functionalities, including EHR communication, telemedicine, and complex clinical decision systems (CCDS), but large-scale users still find it challenging to maintain interoperability, stability, and scalability. Accommodating an extensive array of stakeholders, which includes patients, doctors, hospitals, and laboratories, demands interoperability to serve scalable services. FHIR frameworks have played a vital role in e-Healthcare designs. Most of the existing HL7-FHIR frameworks have used REST-API using HTTP-query for CRUD tasks that impose numerous rules and constraints, making the process more complex and time-consuming, violating the quality-of-service (QoS) standards on different levels. This paper develops a novel, robust Smart-Contract Authentication Assisted HL7-FHIR framework toward an interoperable e-Healthcare solution. Unlike classical REST API-based FHIR, our proposed method applies a Graph-mapping concept that transforms each resource variable into an equivalent Graph-Mapped Data Structure (GMS), which is subsequently stored in the NoSQL MongoDB database, reducing computational costs and time to meet QoS demands. The proposed model employs three key components, GMS-driven HL7 FHIR Gateway Model, Smart Contract Authentication and Client Model. The Smart Contract function helped verify and authenticate users to ensure privacy and secure EHR exchange. The assessment of the performance of the proposed model reveals a significant reduction in computational time with optimal resource utilization making it a significant and viable option to better the real-world e-Healthcare mechanisms.

Qinghai Miao, Wenbo Zheng, Yisheng Lv, Min Huang · 6 authors

From AlphaGo to ChatGPT, the field of AI has launched a series of remarkable achievements in recent years. Analyzing, comparing, and summarizing these achievements at the paradigm level is important for future AI innovation, but has not received sufficient attention. In this paper, we give an overview and perspective on machine learning paradigms. First, we propose a paradigm taxonomy with three levels and seven dimensions from a knowledge perspective. Accordingly, we give an overview on three basic and twelve extended learning paradigms, such as Ensemble Learning, Transfer Learning, etc., with figures in unified style. We further analyze three advanced paradigms, i.e., AlphaGo, AlphaFold and ChatGPT. Second, to enable more efficient and effective scientific discovery, we propose to build a new ecosystem that drives AI paradigm shifts through the decentralized science (DeSci) movement based on decentralized autonomous organization (DAO). To this end, we design the Hanoi framework, which integrates human factors, parallel intelligence based on a combination of artificial systems and the natural world, and the DAO to inspire AI innovations.

Yinqiu Liu, Hongyang Du, Dusit Niyato, Jiawen Kang · 8 authors

The rapid development of Artificial Intelligence-Generated 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.