IET Blockchain

The aim of this study is to design and implement a system that allows centralized blockchain institutions to prove their solvency. This system ensures that institutions do not misappropriate user assets and enhances trust between users and institutions. The article introduces the Groth‐16 zero‐knowledge proof algorithm from ZK‐SNARK (zero‐knowledge succinct non‐interactive argument of knowledge). The R1CS arithmetic circuit in the Groth‐16 algorithm effectively guarantees the authenticity and tamper‐resistance of the system's raw data sources. Additionally, it combines the use of Merkle Sum Trees and Sparse Merkle trees. The former enables users to perform distributed verification of solvency proofs, while the latter effectively hides the overall number of users. Finally, users verify the balances and the private key signatures of addresses in the institution's bulletin board. Together, these components form a comprehensive and distributed solvency proof solution. This solution is a pioneering solution in the field of blockchain solvency proofs and provides a secure, efficient, and privacy‐preserving method for centralized cryptocurrency service providers or Web3 enterprise custodians. It effectively addresses the challenge of proving an institution's possession of sufficient reserves to cover user assets without compromising user privacy or disclosing the institution's scale.

Blockchain technology ensures accountability, transparency, and redundancy, but its reliance on public‐key cryptography makes it vulnerable to quantum computing threats. This article addresses the urgent need for quantum‐safe blockchain solutions by integrating post‐quantum cryptography (PQC) into blockchain frameworks. Utilizing algorithms from the NIST PQC standardization process, it is aimed to fortify blockchain security and resilience, particularly for IoT and embedded systems. Despite the importance of PQC, its implementation in blockchain systems tailored for embedded environments remains underexplored. A quantum‐secure blockchain architecture is proposed, evaluating various PQC primitives and optimizing transaction sizes through techniques such as public‐key recovery for Falcon, achieving up to 17% reduction in transaction size. The analysis identifies Falcon‐512 as the most suitable algorithm for quantum‐secure blockchains in computer‐based environments and XMSS as a viable but unsatisfactory stateful alternative. However, for embedded‐based blockchains, Dilithium demonstrates a higher transactions‐per‐second (TPS) rate compared to Falcon, primarily due to Falcon's slower signing performance on ARM CPUs. This highlights the signing time as a critical limiting factor within embedded blockchains. Additionally, smart contract functionality is integrated, assessing the impact of PQC on smart contract authentication. The findings demonstrate the feasibility and practicality, paving the way for robust and future‐proof IoT applications.

In the new era of adopting and managing new and robust technologies in banking, the use of blockchain technology has significantly transformed overall banking systems. To add new insights to the body of existing knowledge, the authors conducted a systematic review with bibliographic network mapping to identify and analyse the factors contributing to adopting blockchain in the banking industry. Following the latest protocols of the PRISMA flowchart, this study acknowledged 16 relevant publications from 2590 papers in the databases, namely Scopus, ScienceDirect, Web of Science, and IEEE Xplore. The bibliographic data were grouped and analysed using VOSviewer to create network visualization maps that included citation and co‐citation, bibliographic coupling, co‐authorship, and co‐occurrence of terms. Subsequently, significant terms were identified through the analyses and compared with those found in the 16 relevant papers. The aggregate findings suggest that multiple influencing factors have been recognized and later categorized into three thematic drivers: transparency‐driven security, collaborative interoperability, and organizational infrastructure. The current research provides valuable insights for policymakers, technologists, researchers, consultants, and practitioners of information systems by proposing a technological framework, which will aid in developing tailored strategies to facilitate the sustainable practice of blockchain in the banking industry to a wider extent.

The National Scholarship Portal in India serves as a one‐stop solution for students seeking financial aid for their studies across the country. However, in this digital era, the national‐level portal faces challenges such as limited provision for only government scholarships, non‐automated systems, complex application processes, reliance on physical verifications, and delays in scholarship disbursement. This research proposes a blockchain‐based scholarship module to address these challenges and automate the entire scholarship process. The paper emphasizes upon the transformative impact by the usage of Hyperledger fabric network, which provides a fool‐proof system that streamlines the entire application process and fund disbursement. The proposed integration also ensures robust application verification, accountability of stakeholders, transparent scholarship selection criteria, automated and thorough tracking of fund disbursement, immutable transaction history, secure authorization; and stringent compliance measures. Thus, the implementation of the proposed system aims to alleviate the financial insecurities faced by students during their studies, simplify their search for scholarship opportunities, and enable them to focus more on their academic pursuits.

The generative Artificial Intelligence (AI) tools based on Large Language Models (LLMs) use billions of parameters to extensively analyse large datasets and extract critical information such as context, specific details, identifying information, use this information in the training process, and generate responses for the requested queries. The extracted data also contain sensitive information, seriously threatening user privacy and reluctance to use such tools. This article proposes the conceptual model called PrivChatGPT, a privacy‐preserving model for LLMs consisting of two main components, that is, preserving user privacy during the data curation/pre‐processing and preserving private context and the private training process for large‐scale data. To demonstrate the applicability of PrivChatGPT, it is shown how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy; specifically, differential privacy and private training using Reinforcement Learning (RL) were employed. The privacy level probabilities are associated with the document contents, including the private contextual information, and with metadata, which is used to evaluate the disclosure probability loss for an individual's private information. The privacy loss is measured and the measure of uncertainty or randomness is evaluated using entropy once differential privacy is applied. It recursively evaluates the level of privacy guarantees and the uncertainty of public databases and resources during each update when new information is added for training purposes. To critically evaluate the use of differential privacy for private LLMs, other mechanisms were hypothetically compared such as Blockchain, private information retrieval, randomisation, obfuscation, anonymisation, and the use of Tor for various performance measures such as the model performance and accuracy, computational complexity, privacy vs. utility, training latency, vulnerability to attacks, and resource consumption. It is concluded that differential privacy, randomisation, and obfuscation can impact the training models' utility and performance; conversely, using Tor, Blockchain, and Private Information Retrieval (PIR) may introduce additional computational complexity and high training latency. It is believed that the proposed model could be used as a benchmark for privacy‐preserving LLMs for generative AI tools.

On‐chain sealed auctions represent a novel approach to electronic bidding auctions, wherein the introduction of zero‐knowledge proof technology has significantly enhanced the security of auctions. However, most mainstream on‐chain sealed auction schemes currently employ Bulletproofs to prove auction correctness, which leaves room for optimization in terms of verification time and inherent security. Addressing these issues, an on‐chain sealed auction scheme based on zero‐knowledge succinct non‐interactive argument of knowledge (zk‐STARK) is proposed. This scheme leverages the decentralization and immutability of blockchain and smart contracts to eliminate third‐party involvement while ensuring the security of the auction process. The Inter Planetary File System is utilized to provide a qualification review mechanism for the auctioneer, enabling the screening of unqualified bidders before the auction. Additionally, the scheme employs RSA encryption to conceal bidders' bids, Pedersen commitments to ensure the consistency of bidding information, and zk‐STARKs to verify the correctness of the winning bid. Security analysis and experimental results demonstrate that the proposed scheme meets the required security standards, with time consumption at various stages of the auction being within acceptable limits, and effectively reduces the time required for proof verification.

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.

The rapid and green energy transition is essential to deal with the fast‐growing energy needs in both public and industrial sectors. This has paved the way to integrate distributed renewable energy resources (DERs$\mathbb{D}\mathbb{E}\mathbb{R}\mathbb{s}$) such as solar, hydro, wind, and geothermal into the power grid (PG$\mathbb{P}\mathbb{G}$). Wind and solar are free, zero‐carbon emission, and everlasting power sources that contribute 5% and 7% of global electricity generation, respectively. Therefore, the fast, secure, and reliable integration of these green DERs$\mathbb{D}\mathbb{E}\mathbb{R}\mathbb{s}$ is critical to achieve the instant energy demands. Smart grid (SG)$( {\mathbb{S}\mathbb{G}} )$ due to inherited characteristics such as intelligent sensing, computing, and communication technologies can effectively integrate the DERs$\mathbb{D}\mathbb{E}\mathbb{R}\mathbb{s}$. However, the existing smart grid communication architecture faces various cyberattacks, resulting in poor integration, monitoring, and control of DERs$\mathbb{D}\mathbb{E}\mathbb{R}\mathbb{s}$. In this respect, blockchain technology can provide fast, secure, and efficient end‐to‐end communication between DERs$\mathbb{D}\mathbb{E}\mathbb{R}\mathbb{s}$ in the smart grid. In this study, the authors propose a blockchain‐based resilient and secure scheme called (ABCD)$( {\mathbb{A}\mathbb{B}\mathbb{C}\mathbb{D}} )$ for wireless sensor networks (WSNs)$( {\mathbb{W}\mathbb{S}\mathbb{N}\mathbb{s}} )$‐based events monitoring and control in DERs$\mathbb{D}\mathbb{E}\mathbb{R}\mathbb{s}$. Experimental studies and performance analyses are carried out to predict the efficiency of the proposed scheme by considering numerous standard metrics. The extensive numerical results demonstrated that the proposed scheme is significant in terms of secure, resilient, and reliable information transmission for DERs$\mathbb{D}\mathbb{E}\mathbb{R}\mathbb{s}$ in SG$\mathbb{S}\mathbb{G}$.

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.

In recent years, the Metaverse has gained attention as a hub for technological revolution. However, its main platform suffers from issues like low‐quality content and lackluster virtual environments, leading to subpar user experiences. Concerns arise from declining interest in NFTs and failed virtual real estate ventures, casting doubt on the Metaverse's future. Artificial intelligence generated content (AIGC) emerges as a key driver of Metaverse advancement, using AI to create digital content efficiently and affordably. AIGC also enables personalized content, enhancing the Metaverse. This paper examines the link between the Metaverse and AIGC, exploring AIGC's applications, underlying technologies, and future challenges. It reveals that while AIGC shows promise for improving the Metaverse, its technologies must better align with development needs to deliver immersive experiences.

Logistics supply chain (LSC), a chain structure that integrates and coordinates all logistics transactions, has become an essential component of the modern logistics industry. By using blockchain, trusted logistics services enable participants to effectively record and track transactions during the logistics process. Current blockchain‐based LSC features distributed structure and data privacy requirements, hindering the supervision of logistics transactions. Leveraging emerging dual‐blockchain architecture to separate logistics transactions from supervision is a promising direction. However, the dual‐blockchain collaboration restricts supervision due to its cross‐chain privacy and efficiency. To address these issues, a logistics supply chain supervision scheme based on dual‐blockchain collaboration (DBC) is proposed. First, an independent supervision blockchain is constructed to balance the contradiction between distributed structure and supervision requirements. Second, two mechanisms are designed to enhance the privacy and performance of collaborative supervision. The hybrid access control mechanism enables fine‐grained supervision for different participants, and the aggregated transaction verification method supports efficient collaboration for logistics transactions. Security analysis and performance evaluation demonstrate the feasibility of DBC in enhancing the security and supervision of logistics data on the dual‐blockchain architecture. Experimental results show that the cross‐chain supervision overhead of DBC is reduced to 1/n1/n of the baseline schemes.

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.

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.

Addressing the scalability issues, excessive communication overhead, and challenges in adapting to large‐scale network node environments faced by the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm currently employed in consortium blockchains, this paper proposes a Double Layer Consensus Algorithm Based on RAFT and PBFT Consensus Algorithms (DLCA_R_P). The nodes in the blockchain are initially divided into several groups to form the lower‐layer consensus network. Subsequently, the leaders of these groups constitute the upper‐layer consensus network, creating a dual‐layer consensus network structure. Within the lower‐layer consensus network, the PBFT consensus algorithm is employed for consensus among the groups, while the primary accountants form the upper‐layer RAFT consensus network. The algorithm incorporates a supervision mechanism and a reputation mechanism to enhance the security of the consensus network. Additionally, a grouping mechanism is introduced to transform the consensus network into a dynamic structure. Experimental results analysis demonstrates that compared to traditional PBFT consensus algorithms, DLCA_R_P reduces consensus latency by two orders of magnitude and improves throughput by one order of magnitude in a scenario with 100 nodes. Furthermore, it exhibits significant advantages over other improved algorithms. Thus, the DLCA_R_P consensus algorithm exhibits excellent scalability and can be widely applied in various scenarios within consortium blockchains.

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.

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.