IET Blockchain最新文献

The production, distribution of the counterfeit drugs are the major issue in today's world. These drugs create a big economic crunch in today's economic world. The major concern now days the counterfeit of drug is increasing day by day. Counterfeit refers to the fake medicine which is now increasing. In 2020 where all the countries are suffering from counterfeit of medicine. The article discusses about how the secure transaction of drug can be made to the all suppliers. Blockchain is a distributed technology where all the transaction made is transparent. All the transaction made in blockchain will be stored with all the network that relate to the blockchain network. Blockchain contain the concept of hash function. Hash function works like linked list it contains the address of previous node and the next node also. Hash function contain the hexadecimal value. With the implementation of blockchain technology and hyperledger framework it can be able to detect the counterfeit of drug in the early stage. If it is found in the early stage the supply of counterfeit of drug in the pharmaceutical industry like hospitals, pharmacy shops can be stopped. To make the supply of drug secure the hyperledger framework of blockchain is implemented.

In the world of healthcare, joining machine learning with block chain tech offers a smart path for future predictions. The study aims on guessing what kinds of transactions happen in healthcare data stored on a block chain. Machine learning is used to sort these transactions right. This helps make healthcare tasks work on their own and do better. Health data is taken from block chains, looked at it closely, and ran various algorithms on it. Using features such as operation, date, and symbolic indicators, logistic regression, decision tree, random forest, and support vector machine are applied to classify transaction types. The decision tree algorithm achieved the highest accuracy at 89.29%, followed by random forest at 67.86%, logistic regression at 33.93%, and support vector machine at 39.29%. The findings demonstrate the effectiveness of machine learning in improving transaction classification within secure, decentralized medical data environments.

In today's rapidly evolving technological landscape, the healthcare industry is a pivotal sector that demands continuous innovation. Although extensive research on blockchain technology exists, the literature has predominantly emphasized technical aspects and specific applications within industries. However, a comprehensive exploration of blockchain's business and patient value in healthcare remains limited. This study aims to address this gap by conducting a systematic literature review using the PRISMA framework, analyzing highly cited articles from reputable journals. The findings highlight blockchain's potential to revolutionize patient care and optimize business practices within the healthcare sector. This research provides valuable insights for stakeholders, emphasizing the transformative power of blockchain technology to foster innovation, particularly in public healthcare institutions. The results are presented through a value chain analysis and a mindmap, offering a clear framework that can guide researchers and developers in considering the value of blockchain use cases from multiple perspectives.

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region.

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.