【Author】 Wang, Ran Xu, Cheng Zhang, Shuhao Ye, Fangwen Tang, Yusen Tang, Sisui Zhang, Hangning Du, Wendi Zhang, Xiaotong

【Source】NATURE COMMUNICATIONS

【影响因子】17.694

【主题类别】

区块链技术-协同技术-深度学习

【Abstract】The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additionally, the heterogeneous and non-independent and identically distributed (non-i.i.d.) nature of material data hinders model accuracy and generalization in collaborative computing. In this paper, we introduce the MatSwarm framework, built on swarm learning, which integrates federated learning with blockchain technology. MatSwarm features two key innovations: a swarm transfer learning method with a regularization term to enhance the alignment of local model parameters, and the use of Trusted Execution Environments (TEE) with Intel SGX for heightened security. These advancements significantly enhance accuracy, generalization, and ensure data confidentiality throughout the model training and aggregation processes. Implemented within the National Material Data Management and Services (NMDMS) platform, MatSwarm has successfully aggregated over 14 million material data entries from more than thirty research institutions across China. The framework has demonstrated superior accuracy and generalization compared to models trained independently by individual institutions. Industry 4.0 requires collaboration among material research institutions, but data silos hinder progress. Here the authors present MatSwarm, a swarm-learning framework that integrates secure computing and data sharing in the National Material Data Management and Services (NMDMS) platform.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1038/s41467-024-53431-x

【Author】 Aslam, Javed Lai, Kee-hung Kim, Yun Bae Treiblmaier, Horst

【Source】JOURNAL OF INNOVATION & KNOWLEDGE

【影响因子】11.219

【主题类别】

区块链应用-实体经济-物流领域

【Abstract】Empirical studies based on detailed, theory-based analyses are essential for a deep understanding of technology adoption. This study provides an overview of blockchain applications in logistics management, employing a comprehensive theoretical framework. Blockchain is considered a critical digital infrastructure for logistics operations due to its distinctive characteristics, including decentralization, transparency, immutability, real-time information sharing, reliability, and end-to-end visibility. These characteristics address many contemporary logistics challenges. The study introduces a research model that integrates the fit-viability model (FVM) and task technology fit theory (TTF), demonstrating blockchain's suitability for enhancing logistics operational functions and sustainability performance. To validate the model, data were collected from logistics managers of 576 companies and analyzed using partial least squares (PLS) regression. This research offers valuable insights for managers, policymakers, and decision-makers on practical challenges and potential solutions in logistics through the application of blockchain. Furthermore, the study demonstrates that the implementation of blockchain can improve the alignment, resilience, transparency, integration, and sustainability of logistics tasks.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Logistics operations; Sustainability; Fit-viability model; Task technology fit theory

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.jik.2024.100611

【Author】 Dimitriadis, Konstantinos A. Koursaros, Demetris Savva, Christos S.

【Source】INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS

【影响因子】8.235

【主题类别】

区块链治理-市场治理-市场分析

【Abstract】This study investigates the dynamic nexus that major international currencies (US dollar, Euro, Japanese yen) exhibit with cryptocurrencies and highly innovative digital money (DeFi and NFT assets) during inflationary periods such as the Russia-Ukraine conflict (from 14 December 2021 until 1 March 2024). The Quantile Vector Autoregressive methodology as in Cunado et al. (2023) and daily data are adopted to investigate the net joint extended dynamic connectedness and network connectedness at lower and upper quantiles. Conventional international currencies act as hedgers against shocks while major cryptocurrencies are only modest generators with Ripple being an influential absorber of effects. DeFi mainly serve for counteracting losses from conventional investments in bear or bull markets and Maker is the most prominent generator of spillovers while NFTs mostly rely on a few very strong leaders -Gala being by far the strongest- to have an impact, imitating Bitcoin in the early cryptocurrency era.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】US dollar; Euro; Decetralized Finance; Non-Fungible Tokens; Dynamic connectedness

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.irfa.2024.103693

【Author】 Joubert, Thomas H. A.

【Source】INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS

【影响因子】8.235

【主题类别】

区块链治理-市场治理-价格预测

【Abstract】Despite the widespread interest in Bitcoin, a universally accepted model explaining its value remains elusive. This article address a cause to this problem. The best-performing model would not be stable over time due to the fact that Bitcoin can be duplicated. To investigate this hypothesis, I designed study periods based on statistical characteristics and duplication dates. Then, I estimated econometric models over these periods. Results reveal that duplications play a significant and systematic role in the changes in Bitcoin price formation. Furthermore, new variables in the literature are found to be relevant. I also show that the prices of Bitcoin's different versions are uncorrelated after a disjunction but become positively and strongly correlated after several months.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Bitcoin; Cryptocurrency; Asset pricing; Hard fork; Bitcoin futures

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.irfa.2024.103662

【Author】 Rajagopal, Shinu M. Supriya, M. Buyya, Rajkumar

【Source】JOURNAL OF NETWORK AND COMPUTER APPLICATIONS CCF-C

【影响因子】7.574

【主题类别】

区块链应用-实体经济-物联网

【Abstract】Blockchain technology combined with Federated Learning (FL) offers a promising solution for enhancing privacy, security, and efficiency in medical IoT applications across edge, fog, and cloud computing environments. This approach enables multiple medical IoT devices at the network edge to collaboratively train a global machine learning model without sharing raw data, addressing privacy concerns associated with centralized data storage. This paper presents a blockchain and FL-based Smart Decision Making framework for ECG data in microservice-based IoT medical applications. Leveraging edge/fog computing for real-time critical applications, the framework implements a FL model across edge, fog, and cloud layers. Evaluation criteria including energy consumption, latency, execution time, cost, and network usage show that edge-based deployment outperforms fog and cloud, with significant advantages in energy consumption (0.1% vs. Fog, 0.9% vs. Cloud), network usage (1.1% vs. Fog, 31% vs. Cloud), cost (3% vs. Fog, 20% vs. Cloud), execution time (16% vs. Fog, 28% vs. Cloud), and latency (1% vs. Fog, 79% vs. Cloud).

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Edge computing; Federated learning; Fog computing; Internet of Things

【发表时间】2025

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.jnca.2024.104037

【Author】 Ismayilov, Goshgar Ozturan, Can

【Source】JOURNAL OF NETWORK AND COMPUTER APPLICATIONS CCF-C

【影响因子】7.574

【主题类别】

区块链技术-核心技术-零知识证明

【Abstract】Blockchains are decentralized and immutable databases that are shared among the nodes of the network. Although blockchains have attracted a great scale of attention in the recent years by disrupting the traditional financial systems, the transaction privacy is still a challenging issue that needs to be addressed and analyzed. We propose a P rivate T oken T ransfer S ystem (PTTS) for the Ethereum public blockchain in the first part of this paper. For the proposed framework, zero-knowledge based protocol has been designed using Zokrates and integrated into our private token smart contract. With the help of web user interface designed, the end users can interact with the smart contract without any third-party setup. In the second part of the paper, we provide security and privacy analysis including the replay attack and the balance range privacy attack which has been modeled as a network flow problem. It is shown that incase some balance ranges are deliberately leaked out to particular organizations or adversarial entities, it is possible to extract meaningful information about the user balances by employing minimum cost flow network algorithms that have polynomial complexity. The experimental study reports the Ethereum gas consumption and proof generation times for the proposed framework. It also reports network solution times and goodness rates for a subset of addresses under the balance range privacy attack with respect to number of addresses, number of transactions and ratio of leaked transfer transaction amounts.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Zero-knowledge proof; Security; Privacy; Network flow

【发表时间】2025

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.jnca.2024.104045

【Author】 Rathod, Tejal Jadav, Nilesh Kumar Tanwar, Sudeep Alabdulatif, Abdulatif Garg, Deepak Singh, Anupam

【Source】INFORMATION PROCESSING & MANAGEMENT

【影响因子】7.466

【主题类别】

区块链治理-技术治理-治理逻辑

【Abstract】Social engineering attacks are inevitable and imperil the integrity, security, and confidentiality of the information used on social media platforms. Prominent technologies, such as blockchain, artificial intelligence (AI), and proactive access controls, were adopted in the literature to confront the social engineering attacks on social media. Nevertheless, a comprehensive survey on this topic is notably absent from the current body of research. Inspired by that, we propose an exhaustive survey comprising an in-depth analysis of 10 distinct social engineering attacks with their real-time scenarios. Furthermore, a detailed solution taxonomy is presented, offering valuable insights (e.g., objective, methodology, and results) to tackle social engineering attacks effectively. Based on the solution taxonomy, we propose an AI and blockchain-based malicious uniform resource locator (URL) detection framework (as a case study) to confront social engineering attacks on the Meta platform. For that, a standard dataset is utilized, which comprises 12 different datasets containing 3980870 malicious and non-malicious URLs. To classify URLs, a binary classification problem is formulated and solved by using different AI classifiers, such as Naive Bayes (NB), decision tree (DT), support vector machine (SVM), and boosted tree (BT). The non-malicious URLs are forwarded to the blockchain network to ensure secure storage, strengthening the effectiveness of the malicious URL detection framework. The proposed framework is evaluated with baseline approaches, wherein the NB achieves noteworthy training accuracy, i.e., 76.87% and training time of (8.23 (s)). Additionally, interplanetary file system (IPFS)-based blockchain achieves a remarkable response time, i.e., (0.245 (ms)) compared to the conventional blockchain technology. We also used execution cost and smart contract vulnerability assessment using Slither to showcase the outperformance of blockchain technology. Lastly, we shed light on the open issues and research challenges of social engineering attacks where research gaps still exist and require further investigation.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Social engineering attack; Cybersecurity; Phishing; Artificial intelligence; Machine learning; Blockchain

【发表时间】2025

【收录时间】2024-11-16

【文献类型】 综述

【DOI】 10.1016/j.ipm.2024.103928

【Author】 Newell, Jared Rehman, Sabih ur Mamun, Quazi Islam, Md Zahidul

【Source】FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

【影响因子】7.307

【主题类别】

区块链技术-协同技术-数据管理

【Abstract】The weakness of blockchain is widely recognised as the linear, temporal cost required for retrieving data due to the sequential structure of data blocks. To address this, conventional approaches have relied on database indexing techniques applied to each individual replica copy of a blockchain. However, this only partially addresses the problem, because if the index is not distributed it is not available for devices in the blockchain network. If an index is to be incorporated and distributed within blockchain, the unique attribute of immutability necessitates amore innovative approach. To that end, we propose an Enhanced Append-only Skip List (EASL). This specialised indexing technique utilises binary search with skip lists in blockchain, resulting in a sublinear cost for data retrieval. The EASL indexing technique is maintained by each newly appended blockchain block and offers enhanced readability and robustness using an explicitly recorded index structure. Our proposed technique is 42% more efficient in computing and 60% more efficient in storage consumption than its predecessor, the Deterministic Append-only Skip List (DASL) indexing technique. This is achieved through agile data retrieval, resulting in energy cost savings from less computational effort to maintain the index, and less network bandwidth to retrieve blockchain data. The code for the proposed technique is publicly available on GitHub {https://github.com/jarednewell/EASL/}, to expedite future research and encourage the practical application of this effectual data index.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Data index; Append-only skip list; Data management; Distributed ledger technology; Search method; Energy efficiency

【发表时间】2025

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.future.2024.107554

【Author】 Loporchio, Matteo Bernasconi, Anna Maesa, Damiano Di Francesco Ricci, Laura

【Source】FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

【影响因子】7.307

【主题类别】

区块链技术-核心技术-认证机制

【Abstract】Decentralized applications, the driving force behind the new Web3 paradigm, require continuous access to blockchain data. Their adoption, however, is hindered by the constantly increasing size of blockchains and the sequential scan nature of their read operations, which introduce a clear inefficiency bottleneck. Also, the growing amount of data recorded on the blockchain makes resource-constrained light nodes dependent on untrusted full nodes for fetching information, with a consequent need for query authentication protocols ensuring result integrity. Motivated by these reasons, in this paper we propose the skip index, an indexing data structure that allows users to quickly retrieve information simultaneously from multiple blocks of a blockchain. Our solution is also designed to be used as an authenticated data structure to guarantee the integrity of query results for light nodes. We discuss the theoretical properties of skip indices, propose efficient algorithms for their construction and querying, and detail their computational complexity. Finally, we assess the effectiveness of our proposal through an experimental evaluation on the Ethereum blockchain. Asa reference use case, we focus on the popular CryptoKitties application and simulate a scenario where users seek to retrieve the events generated by the service. Our experimental results suggest that the use of skip indices offers a constant multiplicative speedup, thanks to search times that are at most logarithmic within a chosen search window. This allows to reduce the number of visited blocks by up to two orders of magnitude if compared to the naive sequential approach currently in use.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Bloom filter; Smart contract; Authenticated data structure

【发表时间】2025

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.future.2024.107556

【Author】 Bartoletti, Massimo Benetollo, Lorenzo Bugliesi, Michele Crafa, Silvia Dal Sasso, Giacomo Pettinau, Roberto Pinna, Andrea Piras, Mattia Rossi, Sabina Salis, Stefano Spano, Alvise Tkachenko, Viacheslav Tonelli, Roberto Zunino, Roberto

【Source】FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

【影响因子】7.307

【主题类别】

区块链技术-核心技术-智能合约

【Abstract】Smart contracts have played a pivotal role in the evolution of blockchains and Decentralized Applications (DApps). As DApps continue to gain widespread adoption, multiple smart contract languages have been and are being made available to developers, each with its distinctive features, strengths, and weaknesses. In this paper, we examine the smart contract languages used in major blockchain platforms, with the goal of providing a comprehensive assessment of their main properties. Our analysis targets the programming languages rather than the underlying architecture: as a result, while we do consider the interplay between language design and blockchain model, our main focus remains on language-specific features such as usability, programming style, safety and security. To conduct our assessment, we propose an original benchmark which encompasses a wide, yet manageable, spectrum of key use cases that cut across all the smart contract languages under examination.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Smart contracts; Blockchain; Decentralized applications; Cryptocurrencies; Programming languages

【发表时间】2025

【收录时间】2024-11-16

【文献类型】 综述

【DOI】 10.1016/j.future.2024.107563

【Author】 Alkhonaini, Mimouna Abdullah Alohali, Manal Abdullah Aljebreen, Mohammed Eltahir, Majdy M. Alanazi, Meshari H. Yafoz, Ayman Alsini, Raed Khadidos, Alaa O.

【Source】ALEXANDRIA ENGINEERING JOURNAL

【影响因子】6.626

【主题类别】

区块链治理-技术治理-入侵检测

【Abstract】Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Intrusion detection system; Security; Cyberattacks; Blockchain; Internet of Things

【发表时间】2025

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.aej.2024.10.032

【Author】 Chu, Hanting Zhang, Pengcheng Dong, Hai Xiao, Yan Ji, Shunhui

【Source】IEEE TRANSACTIONS ON RELIABILITY CCF-C

【影响因子】5.883

【主题类别】

区块链治理-技术治理-智能合约漏洞检测

【Abstract】Given that smart contracts execute transactions worth hundreds of millions of dollars daily, the issue of smart contract security has attracted considerable attention over the past few years. Traditional methods for detecting vulnerabilities heavily rely on manually developed rules and features, leading to the problems of low accuracy, high false positives, and poor scalability. Although deep learning-inspired approaches were designed to alleviate the problem, most of them rely on monothetic features, which may result in information incompetence during the learning process. Furthermore, the lack of available labeled vulnerability datasets is also a major limitation. To address these issues, we collect and construct a dataset of five labeled smart contract vulnerabilities, and propose DeepFusion, a vulnerability detection method that fuses code representation information, including program slice information and abstraction syntax tree (AST) structured information. First, we develop automated tools to extract contract vulnerability slicing information from source code, and extract structured information from source code-converted AST. Second, code features and global structured features are fused into the data. Finally, the fused data are input into the Bidirectional Long Short-Term Memory+ Attention (BiLSTM+ATT) model for smart contract vulnerability detection. The BiLSTM model can capture long-term dependencies in both directions and is more suitable for processing serialized information generated by DeepFusion, while the attention mechanism can highlight the characteristic information of vulnerabilities. We conducted experiments via collecting a real smart contract dataset. The experimental results show that our method significantly outperforms the existing methods in detecting the vulnerabilities of reentrancy, timestamp dependence, integer overflow and underflow, Use tx.origin for authentication, and Unprotected Self-destruct Instruction by 6.36%, 6.42%, 16.5%, 21.29%, and 25.05%, respectively. To the best of our knowledge, the latter two vulnerabilities are the first to be detected using deep learning methods.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Smart contracts; Data mining; Security; Codes; Predictive models; Syntactics; Computer languages; Arithmetic; Accuracy; Training; Abstraction syntax tree (AST); data fusion; program slicing; smart contract; vulnerability detection

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1109/TR.2024.3480010

【Author】 Alves, P. R. L.

【Source】NONLINEAR DYNAMICS

【影响因子】5.741

【主题类别】

区块链治理-市场治理-市场分析

【Abstract】This work analyses the chaos intensities of the ten cryptocurrencies with larger Volumes in American Dollars from time series in reconstructed phase spaces. In the first step, the routine reveals the chaotic dynamics for the prices in Dollars and Euros and the stochastic behaviour of the financial returns by the quantifiers of chaos and diagrams. The collection of the chaos intensities covers periods before, during and after the COVID-19 health public global crisis and the Russia-Ukraine military conflict. It permits the evaluation of changes in the statistics of chaos measured in these critical times. The statistical methods used to analyse the association between cryptocurrencies are the Spearman Rank Correlation and the Two-Sample T-Test for quotations in dollars and euros. The findings include the statistical evidence against the absence of a correlation between several cryptocurrencies, the maximum chaos intensity during the COVID-19 pandemic and the exception, the insignificance of the Russia-Ukraine conflict in the chaos intensities for all prices studied, and p values in favour of equality of the means of chaos measures in Dollar and Euro.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Econophysics; Chaos; Time series; Cryptocurrencies

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1007/s11071-024-10568-0

【Author】 Wang, Bingkun Guo, Xiaolin

【Source】SUSTAINABLE ENERGY GRIDS & NETWORKS

【影响因子】5.405

【主题类别】

区块链应用-实体经济-能源领域

【Abstract】This paper introduces a novel blockchain-based automatic load response architecture for local energy networks, focusing on secure peer-to-peer (P2P) energy trading and decentralized planning. Departing from traditional centralized methods, the proposed system leverages non-cooperative game theory for pricing-based decentralized planning, enabling efficient resource distribution without a central authority. A key contribution is the integration of a machine-governed smart contract mechanism, which ensures secure, transparent, and consistent transactions in P2P energy trading. Additionally, an adaptive evaluation system for transaction nodes enhances the system's responsiveness to dynamic energy demands. A distributed algorithm is developed to optimize the implementation of this architecture, ensuring practical efficiency. Case studies confirm significant improvements in operational efficiency, security, and economic outcomes, marking a substantial advancement in decentralized energy management. Key findings demonstrate that the proposed automatic load response strategy significantly enhances load curve stability, achieving a 99.16% reduction in net load fluctuations and an 8.24% reduction in operational costs compared to traditional methods. Additionally, the framework improves the self-consumption rate of renewable energy by up to 14.62% and reduces the average cost for electric vehicle (EV) users by 26.12%. These results highlight the framework's effectiveness in fostering a more balanced supply-demand relationship within local energy networks while ensuring economic and computational efficiency. The study underscores the potential to revolutionize decentralized energy management, offering a sustainable and costeffective solution for future energy systems.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Automatic Load Response; Local Energy Grids; Decentralized Programming; Peer-to-Peer Trading

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.segan.2024.101556

【Author】 Wang, Shuang Asif, Muhammad Shahzad, Muhammad Farrukh Ashfaq, Muhammad

【Source】COMPUTERS & SECURITY CCF-B

【影响因子】5.105

【主题类别】

--

【Abstract】In the digital transformation of the banking sector, incorporating advanced technologies such as cloud computing, big data analytics, artificial intelligence, and blockchain has revolutionized financial services. However, this rapid digitalization brings significant data privacy and cybersecurity challenges. This study investigates the challenges banks have maintaining data privacy and cybersecurity while implementing new technologies, how they perceive these challenges, and what steps they take to reduce the risks involved. This qualitative study uses thematic analysis to examine interviews conducted with IT specialists in the banking sector. NVivo 14 software is employed to identify key themes and patterns related to the challenges, perceptions, and strategies regarding data privacy and cybersecurity in technology adoption. The findings reveal that the primary challenges faced by banks include integrating legacy systems, evolving compliance management, managing vendor risks, maintaining customer confidence, and mitigating emerging risks. Banks perceive robust data privacy and cybersecurity as critical for competitive advantage, regulatory compliance, and customer trust. Strategies include robust access controls, continuous threat monitoring, employee training, regulatory compliance with governance frameworks, and data encryption. This study provides original insights into the specific challenges and strategies related to data privacy and cybersecurity faced by banks. It contributes to the existing literature by highlighting the unique context of the banking sector and employing qualitative analysis to uncover nuanced perceptions and practices of IT specialists.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Data privacy; Cybersecurity; Banking industry; Technology adoption; Qualitative analysis

【发表时间】2024

【收录时间】2024-11-16

【文献类型】

【DOI】 10.1016/j.cose.2024.104051

【Author】 Kim, Kyoung Tae Fan, Lu

【Source】INTERNATIONAL JOURNAL OF BANK MARKETING

【影响因子】5.083

【主题类别】

区块链治理-市场治理-市场分析

【Abstract】Purpose - Cryptocurrencies have gained popularity among investors despite their high risk and volatility. Social media wields substantial influence over investors' attitudes, judgments and decisions related to investment. This study aims to investigate the associations between social media usage and cryptocurrency investment behavior. Design/methodology/approach - Utilizing the main dataset of the 2021 National Financial Capability Study and its supplementary Investor Survey, this study analyzed social media usage in general. Additionally, it separately examined 11 different social media platforms as potential sources of information for investments. Logistic regressions were performed to explore the relationship between social media, previous experiences and future considerations in investing in cryptocurrencies. Robustness checks were conducted with additional analyses. Findings - Investors who used social media for investment information were more likely to invest in cryptocurrencies and consider investing in cryptocurrencies in the future. The likelihood increased with the number of social media platforms used. Different social media platforms exhibited distinct associations with cryptocurrency investment experiences and future considerations. Originality/value - This study is one of the initial attempts to examine the role of social media platforms in cryptocurrency investment. The findings offer unique and important theoretical and practical insights for policymakers, researchers and practitioners, which can benefit consumer well-being.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Investment; Cryptocurrency; Social media; Digital assets; Investment knowledge; Risk tolerance

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1108/IJBM-12-2023-0665

【Author】 Dipto, Shakib Mahmud Reza, Md Tanzim Mim, Nadia Tasnim Ksibi, Amel Alsenan, Shrooq Uddin, Jia Samad, Md Abdus

【Source】SCIENTIFIC REPORTS

【影响因子】4.996

【主题类别】

区块链治理-技术治理-异常检测

【Abstract】In recent times, automated detection of diseases from pathological images leveraging Machine Learning (ML) models has become fairly common, where the ML models learn detecting the disease by identifying biomarkers from the images. However, such an approach requires the models to be trained on a vast amount of data, and healthcare organizations often tend to limit access due to privacy concerns. Consequently, collecting data for traditional centralized training becomes challenging. These privacy concerns can be handled by Federation Learning (FL), which builds an unbiased global model from local models trained with client data while maintaining the confidentiality of local data. Using FL, this study solves the problem of centralized data collection by detecting deformations in images of Red Blood Cells (RBC) in a decentralized way. To achieve this, RBC data is used to train multiple Deep Learning (DL) models, and among the various DL models, the most efficient one is considered to be used as the global model inside the FL framework. The FL framework works by copying the global model's weight to the client's local models and then training the local models in client-specific devices to average the weights of the local model back to the global model. In the averaging process, direct averaging is performed and alongside, weighted averaging is also done to weigh the individual local model's contribution according to their performance, keeping the FL framework immune to the effects of bad clients and attacks. In the process, the data of the client remains confidential during training, while the global model learns necessary information. The results of the experiments indicate that there is no significant difference in the performance of the FL method and the best-performing DL model, as the best-performing DL model reaches an accuracy of 96% and the FL environment reaches 94%-95%. This study shows that the FL technique, in comparison to the classic DL methodology, can accomplish confidentiality secured RBC deformation classification from RBC images without substantially diminishing the accuracy of the categorization. Finally, the overall validity of the classification results has been verified by employing GradCam driven Explainable AI techniques.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1038/s41598-024-76359-0

【Author】 Haque, Ehtisham Ul Abbasi, Waseem Almogren, Ahmad Choi, Jaeyoung Altameem, Ayman Rehman, Ateeq Ur Hamam, Habib

【Source】SCIENTIFIC REPORTS

【影响因子】4.996

【主题类别】

区块链技术-核心技术-共识机制

【Abstract】The proliferation of Internet of Things (IoT) devices generates vast amounts of data, traditionally stored, processed, and analyzed using centralized systems, making them susceptible to attacks. Blockchain offers a solution by storing and securing IoT data in a distributed manner. However, the low performance and poor scalability of blockchain technology pose significant challenges for its application in IoT networks. The primary obstacle is the distributed consensus protocol, while ensuring data transparency, integrity, and immutability in a decentralized and untrusted circumstances which often compromises scalability. To address this issue, this paper introduces the use of the Delegated Proof of Stake (DPoS) consensus algorithm and sharding techniques to enhance scalability in blockchain-based IoT networks. Experimental results indicate that system throughput increases synchronously with the test load. Our findings reveal a tradeoff between throughput, latency, and up-downstream time on the Inter Planetary File System (IPFS). Given the critical importance of latency and throughput in IoT networks, the results demonstrate that DPoS offers high throughput, parallel processing, and robust security while efficiently scaling the network. Furthermore, at a test load of 500 Transactions Per Second (TPS), the system achieves a maximum throughput of approximately 11.094 ms. However, when the test load exceeds 2000 TPS, the total processing time for transactions extends to 11.205 ms. This method is particularly suitable for constrained IoT networks. Compared to previous edge computing-based approaches, our scheme demonstrates superior throughput performance.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; Consensus algorithm; Data storage; Internet of things; Parallel execution; Scalability; Sharding; Smart contract

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1038/s41598-024-77706-x

【Author】 Li, Xiao Wu, Weili

【Source】IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS

【影响因子】4.747

【主题类别】

区块链技术-协同技术-边缘计算

【Abstract】Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure to raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to FL in the context. While blockchain technology promises to bolster security, practical deployment on resource-constrained edge devices remains a challenge. Moreover, the exploration of FL with multiple aggregators in edge computing is still new in the literature. Addressing these gaps, we introduce the blockchain-empowered heterogeneous multiaggregator federated learning architecture (BMA-FL). We design a novel lightweight Byzantine consensus mechanism, namely PBCM, to enable secure and fast model aggregation and synchronization in BMA-FL. We study the heterogeneity problem in BMA-FL that the aggregators are associated with varied number of connected trainers with non- IID data distributions and diverse training speed. We propose a multiagent deep reinforcement learning algorithm (MASBDRL) to help aggregators decide the best training strategies. Experiments on real-word datasets demonstrate the efficiency of BMA-FL to achieve better models faster than baselines, showing the efficacy of PBCM and MASB-DRL.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Training; Computational modeling; Computer architecture; Servers; Data models; Peer-to-peer computing; Security; Performance evaluation; Costs; Blockchain; deep reinforcement learning (DRL); distributed machine learning; edge computing; federated learning (FL); federated learning (FL)

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1109/TCSS.2024.3481882

【Author】 Shin, Hyunah Ryu, Kyeongmin Kim, Jong-Yeup Lee, Suehyun

【Source】DIGITAL HEALTH

【影响因子】4.687

【主题类别】

区块链应用-实体经济-医疗领域

【Abstract】With the advent of the big data era, data security issues are becoming more common. Healthcare organizations have more data to use for analysis, but they lose money every year due to their inability to prevent data leakage. To overcome these challenges, research on the use of data protection technologies in healthcare is actively underway, particularly research on state-of-the-art technologies, such as federated learning announced by Google and blockchain technology, which has recently attracted attention. To learn about these research efforts, we explored the research, methods, and limitations of the most widely used privacy technologies. After investigating related papers published between 2017 and 2023 and identifying the latest technology trends, we selected related papers and reviewed related technologies. In the process, four technologies were the focus of this study: blockchain, federated learning, isomorphic encryption, and differential privacy. Overall, our analysis provides researchers with insight into privacy technology research by suggesting the limitations of current privacy technologies and suggesting future research directions.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; differential privacy; federated learning; homomorphic encryption privacy protection; healthcare

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1177/20552076241282242

【Author】 Adamashvili, Nino Spada, Alessia Fiore, Mariantonietta Tricase, Caterina

【Source】SOCIO-ECONOMIC PLANNING SCIENCES

【影响因子】4.641

【主题类别】

区块链治理-市场治理-市场分析

【Abstract】The growing significance of information availability about products for consumers represents a pivotal dimension in contemporary consumer behavior and decision-making processes. In the context of an ever-evolving global marketplace characterized by a huge number of choices, rapid technological advancements and increased consumer awareness, access to comprehensive product information has emerged as a fundamental factor affecting consumers' preferences, choices, and satisfaction levels. There is a significant scientific gap on Italian wine consumers' readiness to adopt emerging technologies. Therefore, the research objective is to investigate whether Italian wine consumers trust and are willing to use emerging technologies such as Quick Response (QR) code and Blockchain Technology (BCT) and to identify the factors influencing their acceptance. By examining and evaluating several crucial aspects in the context of wine purchases in Italy using an online survey, this work seeks to fill the gap in the literature. These crucial elements include customers' propensity for novelty, information-seeking behaviors, faith in new technology, and the intention to use them during wine purchasing. What can be derived is the positive interconnection between consumers novelty-seeking, information-seeking, intention to use the QR code and trust in emerging technologies. With other words, consumers' information-seeking and novelty-seeking is interconnected and they positively determine consumers trust in technologies and their adoption. The implications of this study are twofold. Firstly, it enriches consumer behavior theory by providing new insights into how information availability and emerging technologies influence consumers' decision-making processes, particularly in the context of the Italian wine market. Secondly, businesses can leverage these insights to enhance consumer trust and satisfaction, enabling them to make more effective marketing strategies, improve product transparency, and gain a competitive edge in the increasingly competitive wine market.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Consumers' behavior; Wine; Novelty; Information; QR code; Blockchain

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1016/j.seps.2024.102088

【Author】 Elnahass, Marwa Jia, Xinrui Crawford, Louise

【Source】EMERGING MARKETS REVIEW

【影响因子】4.359

【主题类别】

区块链应用-实体经济-审计领域

【Abstract】We investigate the use of disruptive technology on the level of audit risk, within both companies and audit firms. A sample of FTSE 100 and their corresponding audit firms-specifically, the 'Big 4'-are selected for the period 2015 to 2020. Our findings indicate that the utilisation of disruptive technology results in a significant reduction in audit risk for both companies and audit firms. Disruptive technology seems to promote benefits to companies and audit firms by significantly mitigating the risk of material misstatements (i.e. inherent and control risk) and detection risk; these results are consistent across various industry classifications.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Disruptive technology; Data analytics; Big data; Audit risk; Audit firms

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 综述

【DOI】 10.1016/j.ememar.2024.101218

【Author】 Chuang, Michael Y. Juma'h, Ahmad H.

【Source】TECHNOLOGY ANALYSIS & STRATEGIC MANAGEMENT

【影响因子】3.745

【主题类别】

区块链应用-实体经济-情报领域

【Abstract】Blockchain has emerged as an innovative solution for organisations' data-sharing. This paper utilises bibliometric content analysis to identify key keywords (identity management, privacy, and security issues) related to developments in blockchain adoption. We analysed 105 studies using VOSviewer software, which revealed three significant clusters. Our focus was primarily on the cluster of identity management and data sharing. While the existing literature has addressed privacy and security concerns, our study revealed a major emphasis on privacy issues, with less attention being given to the security aspects associated with potential blockchain consideration. Our discussion delineated the major themes that have arisen, around identity management, data sharing, privacy, security, and consortium blockchain. This paper reveals the link between enterprise blockchain and identity management, including the underlying factors and their interrelationships that highlight the significance of knowledge worthy of attention. These findings have implications for advancing the adoption of enterprise blockchain in a variety of businesses and promote further research.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Blockchain; identity management; data sharing; privacy and security

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1080/09537325.2024.2423800

【Author】 Jose Lopes Gomes, Raimundo Ferreira da Silva, Luciano Rezende da Costa, Priscila Goncalves de Oliveira, Paulo Sergio

【Source】KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE

【影响因子】3.054

【主题类别】

区块链应用-实体经济-工业领域

【Abstract】This article aims to identify how Digital Technologies (DTs) are used in Knowledge Management (KM) in the context of Project Management (PM). A systematic literature review was adopted where 1,554 articles were analysed from Web of Science, and 105 articles were selected for detailed investigation. The articles were categorized by themes and their content was analysed qualitatively, although some quantitative frequency measurements were implemented, including using VOSViewer. The results showed that, especially in the last 4 years, DTs, KM, and PM were treated together with greater relevance. It was identified in the articles that DTs categories used in KM are Industry 4.0, Technological Capacity, Cloud Computing, Big Data, Internet of Things (IoT), Artificial Intelligence, Other Technologies (e.g. Virtual Reality, Augmented Reality, Blockchain, etc.). Furthermore, it was possible to identify how these DTs categories and, mainly, their technological components influence KM, promoting impact in projects context. Thus, this article offers perspectives and solutions to advance understanding the usage of technological components in KM flow to promote impact in projects context.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Digital technologies; knowledge management; project management; projects; industry 4.0; technological capacity

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 综述

【DOI】 10.1080/14778238.2024.2419894

【Author】 Delgoshaei, Aidin Ariffin, Mohd Khairol Anuar

【Source】RAIRO-OPERATIONS RESEARCH

【影响因子】2.526

【主题类别】

区块链应用-实体经济-供应链

【Abstract】Product distribution in supply chain management has been hotly debated during the last decade. However, during COVID-19, many supply chains suffered from sudden changes in local market demands. Such changes cause a bullwhip effect throughout a supply chain, making it unable to respond rapidly. This research develops a new model for distributing products in the food chain using real urban and geographical data of blockchain technology. The aim is to re-adjust the product distribution plans by using a horizontal layer product distribution readjustment strategy while local markets confront sudden market changes. To address the problem, a heuristic was proposed and coded by Python based on the largest density-distance rule. Then, to evaluate the performance of the proposed method, the schedules are assessed with some metrics gathered in the literature. For this purpose, a Full Factorial design of experiments is generated by Python. Moreover, the outcomes are compared with those gained from short-traveling time and greedy loading-based heuristics. The results showed that using the horizontal layer product distribution readjustment strategy for modifying the initial schedules could prevent lost sales in all studied cases. Besides, by responding to sudden market demand changes rapidly, which subsequently prevents lost sales, more profits were gained in 58.3% of the studied cases. In addition, in 61.11% of studied cases, the proposed method was faster than other studied heuristics in terms of computational time.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Supply chain management; real-time scheduling; blockchain; product distribution; density-distance rule

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1051/ro/2024141

【Author】 Mgadmi, Nidhal Abidi, Ameni Hachicha, Nejib Moussa, Wajdi

【Source】COMPUTATIONAL ECONOMICS

【影响因子】1.741

【主题类别】

区块链治理-市场治理-市场分析

【Abstract】Our research stands out for its innovative nature, being the first to delve into the impact of the conflict between Israel and Hamas, as well as the war between Russia and Ukraine, on various markets such as crypto-currencies, stock indices, non-renewable energy prices, and Gold. We introduce the Israel-Hamas war Index and the Russia-Ukraine war Index to assess current Geopolitical Risks (GPR). To support our analysis, we conduct a literature review, examining previous studies addressing the influence of GPR. Our analysis focuses on four specific crypto-currencies: XRP, Bitcoin (BTC), Ethereum (ETH), and USDT. Additionally, we examine seven stock indices: the S&P500, Eurozone index (Euro-Stoxx 50), Moscow stock index (MOEX), Kiev stock index (PETS), UK stock index (FTSE 100), Tel Aviv stock index (TA 35), and Philistine stock index (Al-Quads). Prices of non-renewable energies, including Oil, natural Gas and Gold are also part of our analysis. We modeled each traditional financial asset and crypto-currency using the ARMA model at both level and first difference. We examined the existence or absence of heteroscedasticity and autocorrelation issues, as well as the presence of multiple changes using the Bai-Perron test (1998).We detected symmetric volatility using the FIGARCH model and selected only Ripple and Gold as hedging instruments and safe havens against current GPR. We employ the non-parametric wavelet coherence technique to investigate the Co-movement between these indices, energy prices, and the four crypto-currencies. We validated the close Co-movement between stock indices, non-renewable energy prices, and the two famous crypto-currencies (Bitcoin and Ethereum) during these periods of risk. However, there is a total absence of Co-movement between Ripple and Gold with other traditional assets and crypto-assets.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Energies; Crypto-currencies; Wavelet coherence and armed conflicts; E3; G15; D4

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1007/s10614-024-10776-1

【Author】 Xie, Rui Zhong, Xuejiao Chen, Xin Xu, Shaohui Yu, Haiyang Guo, Xinyuan

【Source】AIP ADVANCES

【影响因子】1.697

【主题类别】

区块链技术-核心技术-智能合约

【Abstract】As an emerging technology, blockchain demonstrates strong potential for applications in digital finance. As a core component of blockchain, the security and reliability of smart contracts is crucial. To ensure the high reliability of smart contracts, this study employs formal construction and verification techniques based on game theory. Initially, the profit function is defined using distortion techniques, and a game model for supply chain participation is designed. However, the equilibrium solution of the two-party game does not represent the optimal solution for the supply chain system. Therefore, the study introduces third-party participation to optimize the equilibrium solution. Finally, a probability model detection method is used to verify the constructed smart contract model. The results show that the supply chain model, analyzed through formal methods, has attributes consistent with theoretical analysis. Consequently, the research on automatic construction and verification algorithms for smart contracts based on formal verification proves to be effective and feasible in practical applications.

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】 10.1063/5.0238456

【Author】 Bildik, Zekeriya Tokmakcioglu, Kaya

【Source】JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING

【影响因子】0.780

【主题类别】

区块链应用-实体经济-金融领域

【Abstract】Purpose: The study aims to establish a correlation between the valuation potential of Initial Public Offerings (IPOs) and a company's enduring competitive advantage. It explores this connection by analyzing micro- economic Key Performance Indicators (KPIs) and corporate dimensions within the financial statements of 42 IPOs listed on the Turkiye Stock Exchange: Borsa Istanbul in 2023 over a three-year period. Design/Methodology/Approach: Employing a novel Triangular Spherical Fuzzy Sets approach, the study assesses the significance of these KPIs and corporate dimensions. This methodology accommodates imprecise and vague data, providing a comprehensive understanding of the indicators. Over three years financial statements, the study involves consultation with three experts to assign importance scores to each indicator. These scores contribute to calculating cumulative scores for the 42 IPOs, subsequently ranking them based on their valuation potential and comparing the theoretical framework with actual valuation performance. A novel ranked space method with genetic algorithm has been applied as a robustness test, confirming the validity of the framework. Findings: The study's results reveal that the research framework developed for the Borsa Istanbul 2023 IPOs may stand as a reliable indicator for long-term IPO performance. Notably, the framework's predictive capacity is observable within a period of less than one year. These findings underscore its value to finance sector stakeholders, empowering investors and analysts with robust decision-making tools. Originality: This research contributes to the field by utilizing a novel Triangular Spherical Fuzzy Sets approach, providing a comprehensive analysis of microeconomic KPIs and corporate dimensions in evaluating IPO valuation potential. The integration of experts' opinions in the assessment process and the framework's remarkable predictive power within a short timeframe underscore the originality and applicability of the study's methodology. Furthermore, the introduction of a novel ranked space method, complemented by a Genetic Algorithm, presents a fresh perspective on measuring IPO valuation potential Research limitations / implications: The study focuses on the 2023 Initial Public Offerings (IPOs) in response to significant shifts in macro trends within the Turkish financial market. These shifts have notably steered investors away from cryptocurrency and towards IPOs, as evidenced by a substantial increase in personal stock exchange investors. Specifically, there was a surge from 3.34 million to 8.32 million investors within the span of a single year in 2023

你可以尝试使用大模型来生成摘要 立即生成

【Keywords】Value investing; Triangular Spherical Fuzzy Sets; long term competitive advantage; IPOs

【发表时间】2024

【收录时间】2024-11-16

【文献类型】 案例研究

【DOI】