IET Blockchain最新文献

Blockchain smart contracts are codes that can execute and enforce rules for blockchain digital transactions. However, smart contracts may contain numerous subtle vulnerabilities, among which Ponzi vulnerabilities are notable. Existing Ponzi scheme contract detection approaches often rely on machine learning models trained on manually extracted features to achieve satisfactory classification results. Nonetheless, the code of a smart contract potentially harbours elusive semantics and characteristics, which compromises the precision and accuracy of vulnerability detection. Therefore, this paper proposes a method of converting operation codes into sequences to process data to avoid losing unnecessary important information, and uses a one-dimensional convolutional neural network combined with formal verification. This method is named PZ-C1DZ3(Ponzi-Conv1D-Z3) and is used for Ponzi scheme detection. Four types of machine learning models, namely Conv1D, Conv1D-LSTM, Conv1D-MLP, and Conv1D-transformer, are employed for improvement and comparative validation experiments. Additionally, formal verification tool Z3 solver is utilized to conduct formal security verification on the final model, ensuring its safety. Experimental results demonstrate that the improved Conv1D model outperforms other existing models in terms of detection efficiency and accuracy while also meeting the requirements of formal security verification.

The modern world has its foundation built on data. Data is generated all the time, and many aspects of the society and economy are built upon it. In today's world, data is employed in better decision-making, innovation, and improving the efficiency of systems. It becomes essential to protect it from being tampered with or misused. Encryptions based on cryptographic systems are used in mainstream data protection these days. There have been newer iterations of mechanisms based on new techniques using blockchain and the zero trust model and even systems that employ fog computing along with content delivery networks. The advent of computation through quantum technologies also helped in developing data security with post-quantum mechanisms. The critical conditions for the working of these mechanisms hinder their adoption primarily. This also leads current systems to lack one or more of these technologies. Thus, the importance of a unified system that can employ quantum technologies effectively becomes prominent and requires a definitive investigation. In this article, a Quantum-IoT-based data protection scheme that can be of value to Industry 4.0 is analyzed. Light is also shed on innovations in quantum computing that can help solve not just current problems but also the future.

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.

This paper presents a novel architecture utilizing blockchain technology to enhance educational systems. A method is proposed that empowers non-fungible tokens by updating their metadata through an API layer, making blockchain technology more accessible to K-12 students. Leveraging Google Blockly, a visual programming language, the aim is to engage students in computational thinking and foster their understanding of blockchain concepts. The approach promotes student engagement, facilitates the comprehension of digital ownership and value, and develops essential skills for the digital age. Through the method, the contribution is in the advancing of blockchain in education. The demo video can be seen using the following link, https://youtu.be/ZPVKgWtSb6U.

Blockchain, as an emerging technology, has found diverse applications in solving trust issues in multi-party cooperation. The field of Unmanned Aerial Vehicle (UAV) swarm networks is no exception, as UAVs are susceptible to attacks from malicious counterparts due to limited computing and storage resources. In this paper, the authors present an adaptive networking mechanism for UAVs based on blockchain technology, which incorporates three key technologies. Utilizing Global Unique Identifier (GUID) and block storage, UAVs can achieve rapid identity authentication and efficient swarm switching. By evaluating node participation in block consensus, malicious nodes within the UAV swarm can be accurately identified. Additionally, routing learning based on node security attributes and link quality ensures secure and efficient data transmission. Simulation test results demonstrate the practicality and applicability of the proposed blockchain-based adaptive networking mechanism for UAV swarm networks.

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.

The blockchain does not need the central organization to make trust endorsement, which is suitable for large scale information exchange between different regions. In addition, blockchain has the characteristics of decentralization and tamper resistant, so that it no longer needs an intermediary platform. A blockchain-based user authorization management method is proposed, which applies blockchain as an underlaying security mechanism for distributed user authorization management within a trusted community of a distributed information exchange sites of federation of industry and commerce. This method can ensure the reliability, consistency, and integrity of user authorization information. With this method, an elastic data exchange trusted community can be built between the data owner and the authorized data user according to the data exchange needs to achieve reliable distributed access authorization.