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Cyber security in the banking sector is of high importance nowadays. The rate of cyberattacks is spiking every year, and the implementation of strong cybersecurity models is required to ensure the confidentiality and integrity of data. Since protecting a bank requires a wide range of security practices, this paper focuses on protecting the bank resources from malicious actors and securing the transactions using a blockchain consensus mechanism that uses a zero‐trust security approach among the participants in the transaction. In addition to the framework, an algorithm for blockchain‐based online transactions was designed to make use of practical implementation in the future. The ideas formulated during the research and literature review were integrated to design the framework and the algorithm. The proposed framework ensures that the security of the banking sector can be enhanced by adopting the zero‐trust concept and blockchain technology. The consensus algorithms used for the transaction make it immutable and decentralized. Zero‐trust principles adopted in the model ensure the confidentiality and integrity of the banking system. This paper proposes a security framework to enhance the security of the banking sector by using a composite design integrating the zero‐trust concept with blockchain technology. The paper outlines three algorithms that ensure the confidentiality of the transactions and builds trust among the sender, sender's bank, and recipient's bank.

With the rapid development of Compute‐First Networking (CFN), network computing resources have become a critical factor in network forwarding, while the centralization defects of traditional Internet trust systems—such as single authentication methods, vulnerability of central nodes, and low scalability—pose significant challenges to network security and transmission efficiency. Blockchain technology, characterized by tamper‐proofing, distributed sharing, and decentralization, offers a novel solution to enhance trustworthiness in CFN. This study aims to construct a network path quality assurance and dynamic trust evaluation mechanism for CFN based on blockchain technology. The goal is to address the centralization issues of traditional systems, improve the reliability of computing resources in network forwarding, and verify the technical feasibility through experimental validation. In the system design phase, it develops blockchain data structures, implements smart contracts, and establishes a network quality monitoring mechanism; In the algorithm optimization phase, it employs a fuzzy algorithm for dynamic node deployment and uses mathematical models (Equations 1‐3) to reduce latency and optimize transmission paths; In the experimental validation phase, it simulates CFN environments in laboratories to compare the performance of blockchain and traditional encrypted communication in terms of latency, bandwidth, and reliability. Experimental results demonstrate that blockchain technology enables more effective backtracking of network states and provides better forwarding paths in CFN environments.Experimental verification confirms that this technology achieves approximately 90% accuracy for network path verification protocols under attack scenarios, surpassing existing solutions, while simultaneously demonstrating superior performance in both latency and bandwidth metrics compared to conventional encryption protocols. This research confirms that blockchain technology effectively resolves centralization issues in traditional trust systems, providing a trustworthy mechanism for CFN network quality assurance. The findings offer technical support for next‐generation Internet trust systems. Future work will focus on deploying the technology in real CFN environments and optimizing algorithms for practical applications.

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. This article investigates blockchain technology's complex ecosystem, focusing on its societal impact, end‐user acceptance, and security aspects. It discusses significant determinants of adoption, privacy issues, legal issues, and security issues. Real‐world case studies highlight the applications of blockchain and provide helpful insights for those involved in academics, business, and government.

This paper introduces an architecture for a next‐generation airline system, utilising cloud‐based microservices, distributed Artificial intelligence (AI), and blockchain to overcome critical system limitations of scale, integrity of data, and avenues of customer inefficiency. In the modular architecture, reservations, payments, and customer profiles can be managed independently, thereby improving scalability by 40% and availability by 30%. AI modules analyse historical data of bookings and user behaviours for demand forecasting and for making personalised recommendations, which, in turn, increases customer engagement by 25%. Blockchain provides proper secure tamper‐proof record‐keeping of transactions, thereby minimising fraud and increasing data transparency by 20%. The proposed system was evaluated under real‐world traffic occurrences, simulating concurrent users in the range of 100–1000, employing a simulation platform that was built for this purpose. The proposed approach reduces transaction latency by 15% and offers a 35% enhancement in throughput for secure data as compared to the usual Systems ‐ Ablation confirms that each module (AI, blockchain, microservices) contributes uniquely to the performance of the system. This architecture holds cross‐domain potential, especially for logistics and hospitality. The findings emphasise the transformation possible when we use AI, blockchain, and cloud services in mission‐critical, high‐demand environments.

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. We propose the conceptual model called PrivChatGPT, a privacy‐preserving model for LLMs that consists of two main components, that is, preserving user privacy during the data curation/pre‐processing together with preserving private context and the private training process for large‐scale data. To demonstrate its applicability, we show how a private mechanism could be integrated into the existing model for training LLMs to protect user privacy; specifically, we employed differential privacy and private training using Reinforcement Learning (RL).

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.

Decentralized autonomous organizations (DAOs) have emerged as a novel governance mechanism that operates through distributed ledgers and smart contracts, enabling members to direct an organization's actions. The widespread adoption of DAOs has occurred in response to their utility in managing emergent semi‐structured projects and has led to the development of various innovative governance mechanisms. The mechanisms employed by DAOs has the potential to be generalized beyond their core financial domain to a wide range of use cases. In the medical field the use of blockchain and DAOs can provide secure and transparent access to medical data, while ensuring patient privacy. Civic access to medical data is a growing area of interest, where individuals have control over their own medical data and can share it with healthcare providers, researchers, and other stakeholders. DAOs can facilitate this civic access, enabling individuals to share their data securely and selectively with authorized parties for research and other purposes. This paper explores the use of DAOs to medical data sharing, with a focus on ownership, governance, and transaction models. An application framework and API that enables the deployment of DAO‐like organizations is derived and this approach is applied to the patient‐centric management of medical data. An investigation into the application of DAO governance mechanisms to the area of civic data management. The paper describes the design and development of an API for managing medical data on the Ethereum blockchain

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. This article presents a novel bidirectional encoder representations from transformers (BERT)‐ATT‐BiLSTM model to enhance smart contract security by accurately detecting vulnerabilities. Utilizing advanced natural language processing techniques, it surpasses traditional methods in accuracy and generalization, significantly reducing financial risks for Dapp users and contributing to the field of blockchain and deep learning.

This article presents algorithms and smart contract designs to facilitate recurring bill payments on decentralised platforms using the Ethereum‐like Virtual Machine (EVM). The proposed approach enables the inclusion of recurring and deferred transactions, ensuring compatibility with ERC‐20 and ERC‐777 standards, thereby contributing to the establishment of a novel token standard. The research addresses the growing need for non‐custodial mechanisms to support automatic periodic payments in decentralised environments, offering potential benefits to both service providers and customers in various industries.

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. This article explores the application of a Consortium Blockchain in cross‐border commercial trade transactions, emphasizing privacy protection. It introduces an innovative approach that combines the +HomEIG Zero‐Knowledge Proof with the SM2 algorithm to enhance transaction security and privacy. This method addresses the challenges of maintaining confidentiality and integrity in cross‐border trade, making it a significant contribution to the field of blockchain‐based trade solutions.