Jiajing Wu, Hong-Ning Dai, Qi Xuan, Radosław Michalski, Xi Chen
<p>Since the birth of blockchain as the underlying support technology for Bitcoin, blockchain technology has received widespread attention from academia and industry worldwide and is considered to have profound potential for disruptive change in areas such as finance, smart manufacturing, and the Internet of Things. As cryptocurrencies, smart contracts, decentralized applications and other derivatives continue to be generated on the blockchain, the volume of transaction data on the blockchain has been maintaining a high growth. With the help of this massive data, we can dig out the development rules of the blockchain, analyze the characteristics of different transactions, and then identify the abnormal behaviour on the blockchain to promote the green and sustainable development of the blockchain. Unfortunately, blockchain transaction data mining faces challenges, such as blockchain data heterogeneity, anonymity and decentralization as well as real-time and generality.</p><p>This special issue aims to provide an open venue for academic and industrial communities to present and discuss cutting-edge technologies and research results regarding blockchain transaction data mining and its applications. It solicits original high-quality papers with new transaction data acquisition tools, transaction network construction and mining methods, anomaly detection algorithms, etc.</p><p>In this Special Issue, we have received eight papers, all of which underwent peer review. Of the eight originally submitted papers, five have been accepted. The overall submissions were of high quality, which marks the success of this Special Issue. A brief presentation of each of the paper in this special issue follows.</p><p>Xiong et al. introduce a graph neural network-based phishing detection method for Ethereum, and conduct extensive experiments on the Ethereum dataset to verify the effectiveness of this scheme in identifying Ethereum phishing detection. The method introduces a feature learning algorithm named TransWalk and constructs an Ethereum phishing fraud detection framework utilizing a transaction-oriented biased sampling strategy for transaction networks and a multi-scale feature extraction method for Ethereum. Through more effective extraction of Ethereum transaction features, the framework aims to enhance phishing fraud detection performance. This work holds significant importance in the field of Ethereum ecosystem security. Access the full paper using the following link: https://doi.org/10.1049/blc2.12031.</p><p>Feng et al. propose a framework for detecting and repairing reentrancy vulnerabilities in smart contracts based on bytecode and vulnerability features. This framework aims to mitigate the losses incurred by reentrancy vulnerabilities in the digital currency economy and offers a more comprehensive solution for detecting and repairing such vulnerabilities. The proposed bytecode-level method overcomes challenges in detection and repair by integrating detecti
他是 IET 研究员和 IEEE 高级会员。他目前的研究兴趣包括物联网、智能电网、人工智能应用和电动汽车充电网络。
{"title":"Blockchain transaction data mining and its applications","authors":"Jiajing Wu, Hong-Ning Dai, Qi Xuan, Radosław Michalski, Xi Chen","doi":"10.1049/blc2.12083","DOIUrl":"https://doi.org/10.1049/blc2.12083","url":null,"abstract":"<p>Since the birth of blockchain as the underlying support technology for Bitcoin, blockchain technology has received widespread attention from academia and industry worldwide and is considered to have profound potential for disruptive change in areas such as finance, smart manufacturing, and the Internet of Things. As cryptocurrencies, smart contracts, decentralized applications and other derivatives continue to be generated on the blockchain, the volume of transaction data on the blockchain has been maintaining a high growth. With the help of this massive data, we can dig out the development rules of the blockchain, analyze the characteristics of different transactions, and then identify the abnormal behaviour on the blockchain to promote the green and sustainable development of the blockchain. Unfortunately, blockchain transaction data mining faces challenges, such as blockchain data heterogeneity, anonymity and decentralization as well as real-time and generality.</p><p>This special issue aims to provide an open venue for academic and industrial communities to present and discuss cutting-edge technologies and research results regarding blockchain transaction data mining and its applications. It solicits original high-quality papers with new transaction data acquisition tools, transaction network construction and mining methods, anomaly detection algorithms, etc.</p><p>In this Special Issue, we have received eight papers, all of which underwent peer review. Of the eight originally submitted papers, five have been accepted. The overall submissions were of high quality, which marks the success of this Special Issue. A brief presentation of each of the paper in this special issue follows.</p><p>Xiong et al. introduce a graph neural network-based phishing detection method for Ethereum, and conduct extensive experiments on the Ethereum dataset to verify the effectiveness of this scheme in identifying Ethereum phishing detection. The method introduces a feature learning algorithm named TransWalk and constructs an Ethereum phishing fraud detection framework utilizing a transaction-oriented biased sampling strategy for transaction networks and a multi-scale feature extraction method for Ethereum. Through more effective extraction of Ethereum transaction features, the framework aims to enhance phishing fraud detection performance. This work holds significant importance in the field of Ethereum ecosystem security. Access the full paper using the following link: https://doi.org/10.1049/blc2.12031.</p><p>Feng et al. propose a framework for detecting and repairing reentrancy vulnerabilities in smart contracts based on bytecode and vulnerability features. This framework aims to mitigate the losses incurred by reentrancy vulnerabilities in the digital currency economy and offers a more comprehensive solution for detecting and repairing such vulnerabilities. The proposed bytecode-level method overcomes challenges in detection and repair by integrating detecti","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"4 3","pages":"223-225"},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/blc2.12083","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142160269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuzhou Chen, Gang Mao, Xue Yang, Mingqian Du, Hongqing Song
Airport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.
{"title":"Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain","authors":"Yuzhou Chen, Gang Mao, Xue Yang, Mingqian Du, Hongqing Song","doi":"10.1049/blc2.12082","DOIUrl":"https://doi.org/10.1049/blc2.12082","url":null,"abstract":"Airport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"76 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141647467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to encourage participants to actively join the data sharing and to meet the distributed structure and privacy requirement in the medical consortium, the data‐sharing strategy based on the master‐slave multichain is presented in this paper. According to the different computing resources and the responsibility of participants, the adaptive Proof of Liveness and Quality consensus and hierarchical federated learning algorithm for master‐slave multichain are proposed. Meanwhile, by quantifying the utility function and the optimization constraint of participants, this paper designs the cooperative incentive mechanism of medical consortium in multi‐leader Stackelberg game to solve the optimal decision and pricing selection of the master‐slave multichain. The simulation experiments show that the proposed methods can decrease the training loss and improve the parameter accuracy by MedMINST datasets, as well as reach the optimal equilibrium in selection and pricing strategy in the system, guaranteeing the fairness of profit distribution for participants in master‐slave multichain.
{"title":"Data‐sharing strategies in medical consortium based on master‐slave multichain and federated learning","authors":"Bohan Kang, Ning Zhang, Jianming Zhu","doi":"10.1049/blc2.12075","DOIUrl":"https://doi.org/10.1049/blc2.12075","url":null,"abstract":"In order to encourage participants to actively join the data sharing and to meet the distributed structure and privacy requirement in the medical consortium, the data‐sharing strategy based on the master‐slave multichain is presented in this paper. According to the different computing resources and the responsibility of participants, the adaptive Proof of Liveness and Quality consensus and hierarchical federated learning algorithm for master‐slave multichain are proposed. Meanwhile, by quantifying the utility function and the optimization constraint of participants, this paper designs the cooperative incentive mechanism of medical consortium in multi‐leader Stackelberg game to solve the optimal decision and pricing selection of the master‐slave multichain. The simulation experiments show that the proposed methods can decrease the training loss and improve the parameter accuracy by MedMINST datasets, as well as reach the optimal equilibrium in selection and pricing strategy in the system, guaranteeing the fairness of profit distribution for participants in master‐slave multichain.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"76 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metaverse is a new ecology that integrates the digital world with the physical world, generates a mirror image of the real world based on digital twin technology, and guarantees the fairness of the virtual world through the trusted mechanism of blockchain. Cross‐chain communication technology is the key to realizing data circulation and collaborative processing between blockchains, which provides the communication foundation for the massive connection of blockchains in the metaverse. The current cross‐chain communication mode is dominated by direct‐connect routing, leading to network congestion and high propagation delay once the direct‐connect link fails and cannot be recovered quickly. To optimize direct‐connect routing, this paper proposed a cross‐chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross‐Chain_RON, which firstly applies RON to reconstruct the direct‐connect routing model, and then selects the optimal link through the shortest‐path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. Finally, the two communication modes were simulated by simulation tools. The experiments show that Cross‐Chain_RON outperforms the direct‐connect routing in terms of latency, rate of successful data transmission, and throughput in poor network environments, but at the expense of a certain amount of system overhead.
元宇宙是数字世界与物理世界融合的新生态，基于数字孪生技术生成现实世界的镜像，并通过区块链的可信机制保证虚拟世界的公平性。跨链通信技术是实现区块链之间数据流通和协同处理的关键，它为元宇宙中区块链的大规模连接提供了通信基础。目前的跨链通信模式以直连式路由为主，一旦直连链路出现故障，无法快速恢复，就会导致网络拥塞和高传播延迟。为了优化直连路由，本文提出了一种基于 RON（Resilient Overlay Network）的跨链路由优化策略，即 Cross-Chain_RON，它首先应用 RON 重构直连路由模型，然后通过最短路径算法和策略路由选择最优链路，并结合 RON 性能数据库提高数据传输效率。最后，利用仿真工具对两种通信模式进行了仿真。实验结果表明，在网络环境较差的情况下，Cross-Chain_RON在延迟、数据传输成功率和吞吐量等方面都优于直连路由，但要以一定的系统开销为代价。
{"title":"RON‐based cross‐chain routing optimization strategy in metaverse","authors":"Junjie Huang, Liang Tan, Jianmei Xiao","doi":"10.1049/blc2.12078","DOIUrl":"https://doi.org/10.1049/blc2.12078","url":null,"abstract":"Metaverse is a new ecology that integrates the digital world with the physical world, generates a mirror image of the real world based on digital twin technology, and guarantees the fairness of the virtual world through the trusted mechanism of blockchain. Cross‐chain communication technology is the key to realizing data circulation and collaborative processing between blockchains, which provides the communication foundation for the massive connection of blockchains in the metaverse. The current cross‐chain communication mode is dominated by direct‐connect routing, leading to network congestion and high propagation delay once the direct‐connect link fails and cannot be recovered quickly. To optimize direct‐connect routing, this paper proposed a cross‐chain routing optimization strategy based on RON (Resilient Overlay Network), that is, Cross‐Chain_RON, which firstly applies RON to reconstruct the direct‐connect routing model, and then selects the optimal link through the shortest‐path algorithm and policy routing, and combines with the RON performance database to improve the data transmission efficiency. Finally, the two communication modes were simulated by simulation tools. The experiments show that Cross‐Chain_RON outperforms the direct‐connect routing in terms of latency, rate of successful data transmission, and throughput in poor network environments, but at the expense of a certain amount of system overhead.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"122 37","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141362481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The focus of this review article is on the societal problems and end user acceptance of blockchain technology. The paper begins by outlining the importance of blockchain in modernizing trust and data management systems and highlighting its rapid spread across numerous industries. In‐depth analysis of the adoption‐influencing aspects is done, which also lists the advantages and typical end‐user problems. It examines the privacy implications, restrictions on pseudonymity, and function of technologies that improve privacy, such as zero‐knowledge proofs, while also exploring the legal and regulatory environment around blockchain, putting a focus on digital identity, intellectual property, and data ownership. It also evaluates blockchain security features, such as flaws and risks associated with smart contracts, discusses best practices for boosting security, discusses the societal effects of blockchain, and makes suggestions for legislators, companies, and scholars. The use of blockchain technology and its effects on privacy, rights, and security are discussed in real‐world case studies as well.
{"title":"Blockchain end user adoption and societal challenges: Exploring privacy, rights, and security dimensions","authors":"Salman Saleem Virani","doi":"10.1049/blc2.12077","DOIUrl":"https://doi.org/10.1049/blc2.12077","url":null,"abstract":"The focus of this review article is on the societal problems and end user acceptance of blockchain technology. The paper begins by outlining the importance of blockchain in modernizing trust and data management systems and highlighting its rapid spread across numerous industries. In‐depth analysis of the adoption‐influencing aspects is done, which also lists the advantages and typical end‐user problems. It examines the privacy implications, restrictions on pseudonymity, and function of technologies that improve privacy, such as zero‐knowledge proofs, while also exploring the legal and regulatory environment around blockchain, putting a focus on digital identity, intellectual property, and data ownership. It also evaluates blockchain security features, such as flaws and risks associated with smart contracts, discusses best practices for boosting security, discusses the societal effects of blockchain, and makes suggestions for legislators, companies, and scholars. The use of blockchain technology and its effects on privacy, rights, and security are discussed in real‐world case studies as well.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141004248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Transport ticketing systems are crucial for enabling seamless, efficient, and sustainable mobility. However, traditional ticketing systems face limitations such as ticket fraud, lack of interoperability, and the inability to adapt to changes in the dynamic transport networks they issue tickets for. This paper presents new approaches to the system for ticketing ubiquity with blockchains (STUB), a novel smart transport ticketing solution that employs ontochains, a hybrid data structure combining blockchains and ontologies to form a type of distributed knowledge graph. STUB aims to address these limitations by providing a secure, transparent, and flexible platform for ticket issuance, validation, and management. We describe the key components and workflow of the STUB system, highlighting the use of transport network ontologies for modelling complex relationships within transportation systems and blockchain technologies for transport network ontology's state. Additionally, the implementation of Merkle proofs for efficient and secure validation between on‐chain and off‐chain ontological data is discussed. Finally, a simulated toy example is used as a lightweight proof‐of‐concept to demonstrate these capabilities. The proposed STUB system has the potential to significantly impact the future of transportation ticketing by offering a more seamless, interoperable, and user‐friendly experience whilst addressing the challenges associated with traditional ticketing systems.
{"title":"Leveraging ontochains for distributed public transit ticketing: An investigation with the system for ticketing ubiquity with blockchains","authors":"J. Preece, Christopher R. Morris, John M. Easton","doi":"10.1049/blc2.12071","DOIUrl":"https://doi.org/10.1049/blc2.12071","url":null,"abstract":"Transport ticketing systems are crucial for enabling seamless, efficient, and sustainable mobility. However, traditional ticketing systems face limitations such as ticket fraud, lack of interoperability, and the inability to adapt to changes in the dynamic transport networks they issue tickets for. This paper presents new approaches to the system for ticketing ubiquity with blockchains (STUB), a novel smart transport ticketing solution that employs ontochains, a hybrid data structure combining blockchains and ontologies to form a type of distributed knowledge graph. STUB aims to address these limitations by providing a secure, transparent, and flexible platform for ticket issuance, validation, and management. We describe the key components and workflow of the STUB system, highlighting the use of transport network ontologies for modelling complex relationships within transportation systems and blockchain technologies for transport network ontology's state. Additionally, the implementation of Merkle proofs for efficient and secure validation between on‐chain and off‐chain ontological data is discussed. Finally, a simulated toy example is used as a lightweight proof‐of‐concept to demonstrate these capabilities. The proposed STUB system has the potential to significantly impact the future of transportation ticketing by offering a more seamless, interoperable, and user‐friendly experience whilst addressing the challenges associated with traditional ticketing systems.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"96 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141003526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The burgeoning interest in decentralized applications (Dapps), spurred by advancements in blockchain technology, underscores the critical role of smart contracts. However, many Dapp users, often without deep knowledge of smart contracts, face financial risks due to hidden vulnerabilities. Traditional methods for detecting these vulnerabilities, including manual inspections and automated static analysis, are plagued by issues such as high rates of false positives and overlooked security flaws. To combat this, the article introduces an innovative approach using the bidirectional encoder representations from transformers (BERT)‐ATT‐BiLSTM model for identifying potential weaknesses in smart contracts. This method leverages the BERT pre‐trained model to discern semantic features from contract opcodes, which are then refined using a Bidirectional Long Short‐Term Memory Network (BiLSTM) and augmented by an attention mechanism that prioritizes critical features. The goal is to improve the model's generalization ability and enhance detection accuracy. Experiments on various publicly available smart contract datasets confirm the model's superior performance, outperforming previous methods in key metrics like accuracy, F1‐score, and recall. This research not only offers a powerful tool to bolster smart contract security, mitigating financial risks for average users, but also serves as a valuable reference for advancements in natural language processing and deep learning.
{"title":"Enhancing smart contract security: Leveraging pre‐trained language models for advanced vulnerability detection","authors":"Fei He, Fei Li, Peili Liang","doi":"10.1049/blc2.12072","DOIUrl":"https://doi.org/10.1049/blc2.12072","url":null,"abstract":"The burgeoning interest in decentralized applications (Dapps), spurred by advancements in blockchain technology, underscores the critical role of smart contracts. However, many Dapp users, often without deep knowledge of smart contracts, face financial risks due to hidden vulnerabilities. Traditional methods for detecting these vulnerabilities, including manual inspections and automated static analysis, are plagued by issues such as high rates of false positives and overlooked security flaws. To combat this, the article introduces an innovative approach using the bidirectional encoder representations from transformers (BERT)‐ATT‐BiLSTM model for identifying potential weaknesses in smart contracts. This method leverages the BERT pre‐trained model to discern semantic features from contract opcodes, which are then refined using a Bidirectional Long Short‐Term Memory Network (BiLSTM) and augmented by an attention mechanism that prioritizes critical features. The goal is to improve the model's generalization ability and enhance detection accuracy. Experiments on various publicly available smart contract datasets confirm the model's superior performance, outperforming previous methods in key metrics like accuracy, F1‐score, and recall. This research not only offers a powerful tool to bolster smart contract security, mitigating financial risks for average users, but also serves as a valuable reference for advancements in natural language processing and deep learning.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"44 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To meet the demand for high‐quality healthcare services, data trading can effectively promote the circulation of medical data and improve the level of healthcare services. To address the existing problems of data regulation difficulties and data privacy leakage in medical data trading, a trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof is proposed. In this scheme, a regulatory institution is introduced to control the issuance of authorized tokens and ensure the controllability of data sharing activities. The blockchain takes over the task of generating public parameters to reduce the computational overhead of the system. Based on homomorphic proxy re‐encryption technology, users can perform data analysis in the cloud to ensure data security. Smart contracts and zero‐knowledge proof technology can automatically verify the validity of data to protect the rights and interests of data users; at the same time, efficient consensus algorithms can also increase the rate of transactions processed by the blockchain system. Finally, as the security and performance analysis shows, the scheme in this paper has better security, higher efficiency and more comprehensive functions.
{"title":"A trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof","authors":"Dongmei Li, Xiaohui Ke, Xiaomei Zhang, Yujin Zhang","doi":"10.1049/blc2.12070","DOIUrl":"https://doi.org/10.1049/blc2.12070","url":null,"abstract":"To meet the demand for high‐quality healthcare services, data trading can effectively promote the circulation of medical data and improve the level of healthcare services. To address the existing problems of data regulation difficulties and data privacy leakage in medical data trading, a trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof is proposed. In this scheme, a regulatory institution is introduced to control the issuance of authorized tokens and ensure the controllability of data sharing activities. The blockchain takes over the task of generating public parameters to reduce the computational overhead of the system. Based on homomorphic proxy re‐encryption technology, users can perform data analysis in the cloud to ensure data security. Smart contracts and zero‐knowledge proof technology can automatically verify the validity of data to protect the rights and interests of data users; at the same time, efficient consensus algorithms can also increase the rate of transactions processed by the blockchain system. Finally, as the security and performance analysis shows, the scheme in this paper has better security, higher efficiency and more comprehensive functions.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"27 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140252141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Counterfeit artwork presents a significant risk to copyright holders and the economy. Without expertise in art, it is not straightforward to distinguish an artwork counterfeit from a genuine piece. This work designs and implements a reliable solution that enables users with near‐field communication‐enabled Android smartphones to verify artwork authenticity by accessing its respective certificate of authenticity stored in the Ethereum blockchain. To represent a physical artwork, the artwork image and metadata are stored in an inter‐planetary file system and minted as an ERC721 non‐fungible token to a smart contract deployed in Ethereum Rinkeby Testnet. The mobile app ArtProtect was developed to generate certificate of authenticity based on each non‐fungible token fetched through OpenSea Testnet API. Users access this information by scanning an near‐field communication tag embedded into the artwork. The content of the tag is signed by the respective artwork's artist and responsible agent by using Ethereum signing with their Ethereum wallet accounts. Through testing and analysis, the implemented work is secure, tamper‐evident, usable, flexible, and inexpensive to be applied to a real‐world scenario.
假冒艺术品给版权持有者和经济带来巨大风险。如果没有艺术方面的专业知识，就无法直接辨别艺术品的真伪。本作品设计并实现了一种可靠的解决方案，使用户能够使用支持近场通信的安卓智能手机，通过访问存储在以太坊区块链中的艺术品鉴定证书来验证艺术品的真伪。为了代表实物艺术品，艺术品的图像和元数据被存储在星际文件系统中，并作为ERC721不可伪造令牌铸入部署在以太坊Rinkeby Testnet中的智能合约中。开发的移动应用程序 ArtProtect 可根据通过 OpenSea Testnet API 获取的每个不可伪造令牌生成真实性证书。用户通过扫描嵌入艺术品中的近场通信标签来获取这些信息。标签的内容由相应艺术品的艺术家和责任代理人通过以太坊钱包账户使用以太坊签署。通过测试和分析，所实现的作品具有安全、防篡改、可用、灵活和廉价的特点，可以应用于现实世界的场景中。
{"title":"ArtProtect: Blockchain and NFC‐based anti‐counterfeit system for physical art","authors":"Cindy Handoko Tantowibowo, Wei‐Chuen Yau","doi":"10.1049/blc2.12069","DOIUrl":"https://doi.org/10.1049/blc2.12069","url":null,"abstract":"Counterfeit artwork presents a significant risk to copyright holders and the economy. Without expertise in art, it is not straightforward to distinguish an artwork counterfeit from a genuine piece. This work designs and implements a reliable solution that enables users with near‐field communication‐enabled Android smartphones to verify artwork authenticity by accessing its respective certificate of authenticity stored in the Ethereum blockchain. To represent a physical artwork, the artwork image and metadata are stored in an inter‐planetary file system and minted as an ERC721 non‐fungible token to a smart contract deployed in Ethereum Rinkeby Testnet. The mobile app ArtProtect was developed to generate certificate of authenticity based on each non‐fungible token fetched through OpenSea Testnet API. Users access this information by scanning an near‐field communication tag embedded into the artwork. The content of the tag is signed by the respective artwork's artist and responsible agent by using Ethereum signing with their Ethereum wallet accounts. Through testing and analysis, the implemented work is secure, tamper‐evident, usable, flexible, and inexpensive to be applied to a real‐world scenario.","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"11 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point-to-point transactions, and decentralized autonomous organizations. Here, the authors focus on blockchain-based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third-party verification by using consensus-based verification. The 12 most popular blockchain platforms are investigated and 6 platforms that are related to finance are elaborated on, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain-based securities trading applications. Numerous practical applications of blockchain-based securities trading are gathered and they are categorized into four distinct categories. For each category, a typical example is introduced and how blockchain contributes to solving the key problems faced by FinTech companies and researchers explained. Finally, interesting observations are provided ranging from mainstream blockchain-based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance.
{"title":"Blockchain for finance: A survey","authors":"Hanjie Wu, Qian Yao, Zhenguang Liu, Butian Huang, Yuan Zhuang, Huayun Tang, Erwu Liu","doi":"10.1049/blc2.12067","DOIUrl":"10.1049/blc2.12067","url":null,"abstract":"<p>As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point-to-point transactions, and decentralized autonomous organizations. Here, the authors focus on blockchain-based securities trading, in which blockchain technology plays a vital role in financial services as it ultimately lifts trust and frees the need for third-party verification by using consensus-based verification. The 12 most popular blockchain platforms are investigated and 6 platforms that are related to finance are elaborated on, seeking to provide a panorama of securities trading practices. Meanwhile, this survey provides a comprehensive summary of blockchain-based securities trading applications. Numerous practical applications of blockchain-based securities trading are gathered and they are categorized into four distinct categories. For each category, a typical example is introduced and how blockchain contributes to solving the key problems faced by FinTech companies and researchers explained. Finally, interesting observations are provided ranging from mainstream blockchain-based financial institutions to security issues of decentralized finance applications, aiming to picture the current blockchain ecosystem in finance.</p>","PeriodicalId":100650,"journal":{"name":"IET Blockchain","volume":"4 2","pages":"101-123"},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/blc2.12067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140425324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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