IET Blockchain最新文献

With an increasing affinity towards patient-centric care, sharing real-time sensitive data for collaboration between multiple parties with finer access control becomes critical. Most existing studies based on the blockchain technology in the medical field discuss various application scenarios and security aspects, without focusing on data ownership, secure data sharing, or finer access control. In this work, a non-fungible token (NFT)-based system is proposed to implement a health record marketplace. The system leverages the NFT technology to provide dual ownership along with finer access control and efficiency in data sharing. The advantage of permissioned blockchain along with InterPlanetary File System (IPFS) are taken for off-chain data storage to improve security and efficiency. Because price determination is critical in the market, Stackelberg game theory is utilized to determine pricing strategies for both data owners and consumers. Also, to efficiently achieve finer access control, a popularity-based adaptive NFT management scheme using reinforcement learning is proposed. Simulation experiments are carried out to demonstrate accuracy and efficiency of our proposed schemes.

Privacy concerns the majority of individuals, governments, and organizations that share data over dissimilar networks of nodes. Every kind of participant requires awareness of the data journey that unfolds inside a blockchain network with its own trustworthy rules of data management and accessibility. This paper provides a methodological approach on privacy for data sharing within blockchain environments. Specific technological aspects of blockchains that relate to on-chain privacy such as network nature, party join, smart contracts, blockchain states, transactions, and ledger flows, are analysed with respect to the involvement and impact of privacy in distributed ledgers. A high-level architectural approach is suggested that is intended to address significant privacy concerns on data sharing among different kinds of users in the context of blockchain networks deployment and the broader Web 3.0 ecosystem building. Simultaneously, many pertinent challenges are discussed regarding network nature, configurable privacy, ownership, confidentiality, secured computation, and data monetization that appear in the field and ought to be carefully tackled for the future mass adoption of privacy-matured distributed ledgers that interconnect delivering the future Internet.

Common federated learning (FL) lacks consideration of clients' personalized requirements, which performs poorly for the scenario with data and resource heterogeneity. In order to overcome the challenge of heterogeneous characteristics, this letter proposes a novel decentralized personalized federated learning (PFL) architecture that first utilizes a directed acyclic graph (DAG) blockchain technology to achieve PFL efficiently, which is called PFLDAG. Simulation results demonstrate that PFLDAG approximately improves accuracy by 80% compared with the classic Google FedAvg algorithm, and by 10% compared with IFCA cluster PFL which considers personalized requirements. In addition, the approach also substantially improves the convergence speed.

Blockchain technology has emerged and evolved as a disruptive technology with the potential to be applied in various fields, including digital finance, healthcare, and the Internet of Things (IoT). Besides being a distributed ledger, blockchain enables decentralized and trusted storage/computation without relying on a central trusted party. However, the growing heterogeneity of blockchain platforms and the expanding range of applications have resulted in escalating security and privacy concerns. These concerns encompass persistent privacy breaches, vulnerabilities in smart contracts, and the “impossible triangle” problem. These challenges have emerged as the primary obstacles to the development and seamless integration of blockchain technology with industry applications.

To address the security and privacy challenges in blockchain platforms and its applications, numerous researchers have conducted extensive studies in this field by leveraging advanced technologies, including new cryptographic protocols and deep learning techniques. This special issue aims to highlight research perspectives, articles, and experimental studies pertaining to “Security and Privacy Issues in Blockchain and Its Applications”.

In this special issue, we received a total of 19 papers, out of which 17 underwent a rigorous peer-review process. However, two papers were excluded from the peer-reviewed selection because one was submitted in a draft form and the other was voluntarily withdrawn by the authors. Out of the 17 papers submitted for review, 10 were accepted for publication, six were rejected without being transferred, and one was rejected and referred to a transfer service. The exceptional quality of all the submissions played a crucial role in ensuring the success of this special issue.

These accepted papers can be classified into two categories, namely blockchain application security and cross-chain interaction security. The papers in the first category focus on analyzing and providing insights into the security of blockchain applications. Their objective is to keep readers informed about the latest trends, developments, challenges, and opportunities in blockchain application security. Moreover, significant research efforts have been dedicated to security analysis and detection in typical blockchain applications. The papers in this category are of Zhou et al., Grybniak et al., Lv et al., Li et al., Gong et al., Xiao et al. and Videira et al. These contributions further enhance our understanding and capability to safeguard blockchain applications from potential security threats. The second category of papers presents novel solutions that target the enhancement of security in cross-system interactions. These papers are of Feng et al., Xu et al. and Yu et al. By addressing the specific challenges associated with cross-system communication, these solutions contribute to the development of robust and secure blockchain networks. A brief presentation

The personal lending marketplace, known as Peer-to-Peer (P2P) lending, has increased globally. However, providing unsecured loans to peers without requiring collateral remains a challenge. A platform called TrustLend is proposed to enable trustworthy transactions in the personal lending application. The platform attempts to eliminate or minimize the collateral requirement. The trustworthiness score adds to this platform's variable selection rules and can help lenders decide on reliable candidates as borrowers. The prototype implementing the TrustLend platform based on Ethereum smart contracts that use the trustworthiness score is also described and it is illustrated with a Decentralized Application (DApp) case study and customized smart contracts. The prototype demonstrates fundamental features and supports borrowers, lenders, and recommenders in establishing proposals and approvals. Finally, the prototype shows how end-users can easily access loans with reduced collateral without hidden costs and swift transactions.

With the rapid development of blockchain technology in the financial sector, the security of blockchain is being put to the test due to an increase in phishing fraud. Therefore, it is essential to study more effective measures and better solutions. Graph models have been proven to provide abundant information for downstream assignments. In this study, a graph-based embedding classification method is proposed for phishing detection on Ethereum by modeling its transaction records using subgraphs. Initially, the transaction data of normal addresses and an equal number of confirmed phishing addresses are collected through web crawling. Multiple subgraphs using the collected transaction records are constructed, with each subgraph containing a target address and its nearby transaction network. To extract features of the addresses, a modified Graph2Vec model called imgraph2vec is designed, which considers block height, timestamp, and amount of transactions. Finally, the Extreme Gradient Boosting (XGBoost) algorithm is employed to detect phishing and normal addresses. The experimental results show that the proposed method achieves good performance in phishing detection, indicating the effectiveness of imgraph2vec in feature acquisition of transaction networks compared to existing models.

A major drawback in deploying central bank digital currencies (CBDC) is the offline puzzle, which requires that a CBDC must keep the liquidity provision given by cash, and, simultaneously, avoid double-spending, cloning, and other issues. The puzzle is solved by minting the coins in serial numbers, which are stored on a local blockchain inside a smartphone or EMV card. The local blockchain is strengthened by a two-stage approval architecture that mitigates attacks and enables non-repudiation handling. The coins are protected by hardware keys embedded in the microchip and can be continuously mined by the wallet to enhance security. The coins can be either minted as hot coins, which can be retrieved in case of loss, or minted as cold coins, like physical cash.