IET Blockchain最新文献

In this paper, we propose the modelling of patterns of financial transactions - with a focus on the domain of cryptocurrencies - as splittings and present a method for generating such splittings utilizing integer partitions. We study current money laundering regulations and directives concerning thresholds for monitoring of financial transactions. We further exemplify that, by having the partitions respect these threshold criteria, the splittings generated from them can be used for modelling illicit transactional behavior such as is shown by smurfing. In addition, we conduct an analysis of the splittings occurring in money laundering efforts that took place in the aftermath of the Upbit hack. Based on the potential weaknesses identified by our research, we finally provide suggestions on how to improve current AML techniques and initiatives towards more effective AML efforts.

This study presents an innovative energy management framework for multi-microgrids, integrating the burgeoning domain of cryptocurrency mining. Cryptocurrencies, a novel fusion of encryption technology and financial currency, are witnessing exponential global growth. This expansion correlates with a surge in the prevalence of mining activities, amplifying electricity consumption and necessitating accelerated advancements in urban transmission and distribution infrastructures, coupled with increased financial investments. Despite cryptocurrencies' growth, comprehensive research to capitalize on their potential is scarce. This article introduces an operation cost model for miners in the proposed dual-stage framework. The first stage is dedicated to day-ahead scheduling, focusing on peak shaving and valley filling in the electricity demand curve, while concurrently optimizing operational costs. The second stage, updating each 5 min, minimizes imbalances in response to uncertain network conditions. A pivotal feature of this framework is the allocation of revenues generated from mining operations towards enhancing renewable energy resources. Empirical simulations underscore the framework's efficacy, evidenced by a substantial peak shaving of 482.833 kW and valley filling of 4084.42 kW. Furthermore, this approach effectively maintains operational costs within a feasible spectrum. Notably, the demand curve's peak-to-valley distance extends to 4 MW, with the revenue from mining activities alone sufficient to offset operational expenditures.

The development of Web 3.0 technology may signify the dawn of a new digital era. Its concepts of co-management, co-construction, and sharing address the need for private data sharing among medical institutions. However, the sharing of private data has been challenging due to the lack of effective monitoring methods and authorization mechanisms. Additionally, controlling the scope of data sharing, providing incentives, and ensuring legal compliance have presented difficulties. To this end, a medical privacy data security sharing model based on key technologies of Web 3.0 has been proposed and implemented. It stores the source data in Inter Planetary File System by constructing an index of private data keywords, generates trapdoors using query keywords, and achieves retrieval of ciphertext data. Finally, data users apply to multiple parties for joint secure computing to obtain the use of private data. The experimental results indicate that when the size of the private data is less than 5 MB, with 3000 ciphertext indexes and three search keywords, both encryption and decryption times are around 50 ms, and the retrieval time is approximately 1.6 s. This performance is adequate for typical medical privacy sharing and computing scenarios.

As the core technology of blockchain, consensus mechanisms play a crucial role in ensuring the consistency and reliability of blockchain systems. In a decentralized and open system environment like blockchain, traditional consensus algorithms are often unsuitable due to their inability to tolerate arbitrary faults such as malicious node behaviour. Consequently, Byzantine fault tolerance consensus algorithms have become a focal point in blockchain systems. However, as Byzantine fault tolerance consensus algorithms have evolved, they still face significant challenges, particularly in addressing issues related to network latency and throughput. This paper proposes a parallel pipeline-based DAG schema for consensus in blockchain, Serein. Firstly, the Serein algorithm achieves functional partitioning of nodes, enhancing their scalability. Secondly, it employs a pipeline structure, allowing each block to proceed without waiting for the previous block's result, thereby reducing block generation latency. Lastly, the Serein algorithm leverages the advantages of the DAG block structure to achieve concurrent block ordering and submission, improving system throughput. Experimental results demonstrate that the proposed Serein algorithm maintains robust performance under conditions of high transaction volume with multiple nodes, effectively enhancing consensus efficiency while ensuring Byzantine fault-tolerant security.

Effective data management is crucial in navigating any health crisis. With proper data management protocols in place, stakeholders can swiftly adapt to evolving circumstances during challenging times. A recent event like the COVID-19 pandemic has unequivocally revealed its significance. It is essential to conduct disease surveillance, practice preventive measures, and devise policies to contain the situation. As the process involves massive data growth, it demands an acute level of oversight and control. Monitoring this vast sensitive data faces multifaceted limitations, namely data tampering, breach of privacy, and centralized data stewardship. In response to these challenges, we propose an innovative blockchain-enabled scalable data management scheme in light of the COVID-19 scenario. However, blockchain cannot scale in a large ecosystem due to storing all contents in every participating node. This work addresses this shortcoming by proposing a lightweight solution that groups nodes into clusters, resulting in less memory and processing overhead. Moreover, it adopts an off-chaining technique to reduce the memory load of every node and, thereby, the entire network. The experimental results demonstrate that it attains approximately 85% and 94% storage reduction per node and the whole network, respectively, and an 87% reduction in transaction processing time.

In response to the dual privacy protection challenges concerning the confidentiality of transaction amounts and identities in cross-border trade, a transaction scheme that combines ⁺HomEIG Zero Knowledge Proof (⁺HomEIG-ZKProof) and the national encryption algorithm SM2 is proposed. While ensuring transaction traceability and verifiability, this scheme achieves privacy protection for both payers’ and recipients’ identities, specifically tailored for cross-border trade scenarios. Additionally, customs authorities play the role of supervisory nodes to verify the identities of transaction parties and the zero-knowledge proofs for transaction information. The RAFT consensus algorithm is employed to construct a secure authentication application, demonstrating how zero-knowledge proofs, combined with homomorphic encryption, can be verified through a consensus process. In this scenario, the legitimacy of transaction amounts is subject to zero-knowledge verification during consensus interactions. Merchant identity verification is accomplished using SM2 ring signatures. The analysis indicates that this scheme offers strong security features such as resistance to tampering attacks, public key replacement attacks, impersonation attacks, and anonymity. Testing results demonstrate that this scheme can effectively provide dual privacy protection for transaction amounts and identities in cross-border trade, meeting the practical requirements of privacy protection in cross-border trade transactions.

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.

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.

Blockchain trilemma is a considerable obstacle for today's decentralized systems. It is hard to achieve a perfect balance among decentralization, security, and scalability. Many popular blockchain platforms sacrifice scalability to preserve decentralization and security, resulting in low speed, reduced throughput, and poor real-time performance (the time from transaction initiation to confirmation). Currently, there are several technologies, such as sharding, directed acyclic graph technology, sidechains, off-chain state channels etc., that aim to improve throughput and real-time performance. However, most of these solutions compromise the core feature of blockchain, which is decentralization, and introduce new security risks. In this paper, the authors propose a novel method, called MEchain, based on the Proof of Time Series Algorithm. MEchain consists of two models: the multi-chain model and the elastic-chain model. The authors’ experimental results show that these two models can enhance real-time performance and throughput to a higher level in the industry.