【Author】 Alzubaidi, Laith Jebur, Sabah Abdulazeez Jaber, Tanya Abdulsattar Mohammed, Mohanad A. Alwzwazy, Haider A. Saihood, Ahmed Gammulle, Harshala Santamaria, Jose Duan, Ye Fookes, Clinton Jurdak, Raja Gu, Yuantong
【影响因子】17.564
【主题类别】
--
【Abstract】Big data and its distributed approach to data management have evolved significantly in recent years, giving rise to a huge volume of data generated from new services, devices (e.g. IoT), and applications. Recently, federated learning (FL) has been proposed for training deep learning models on distributed data in order to address significant challenges previously described in the literature, e.g., those concerns considering privacy, security, computational overhead, and legal restrictions. Nevertheless, while FL adequately addresses the above-mentioned limitations, there are still lingering drawbacks regarding privacy, security, scalability, single point of failure, and conflicting security policies. Specifically, the handling of sensitive data complicates the sharing and utilisation of data without breaching confidentiality. In this work, we propose a novel learning approach, named the AI-To-Data (ATD) learning method, to deal with the previous drawbacks. In particular, ATD proposes a more robust decentralised approach in which AI models are transferred to the data for training rather than centrally aggregating the model's parameters. Each ATD node operates as both a local and a global entity, i.e. training on local data while synthesising models from other nodes. This new approach preserves data locality while enabling collaborative model training, thus fostering a more secure and integrated learning environment. Additionally, Eye on ATD, a blockchain-based security mechanism, is proposed to be incorporated into ATD to address potential security and privacy vulnerabilities, e.g. malicious participants or tampered updates. Our approach based on the combination of ATD and Eye on ATD has been extensively evaluated using three distinct datasets across multiple nodes considering multi-scenarios of abnormal behaviour detection tasks, including violence. The empirical results demonstrated that our proposal outperforms the state-of-the-art by obtaining an average accuracy of 92.1%. It has been carried out an independent test in order to validate the generalisation of ATD, achieving an accuracy of 89.2%. In addition, the scalability of the ATD has been tested by adding a fourth node with different behaviours, including shoplifting. In the last scenario, ATD achieved an accuracy of 93.3% when considering the four nodes without any negative impact on the performance of the entire system. Finally, it is worth highlighting how ATD ensures compliance with various regulatory frameworks due to ATD facilitates seamless node scalability and supports customisable data governance policies. The code of the proposed framework, both ATD and Eye on ATD, is available at https://github.com/LaithAlzubaidi/ATD/tree/main.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Deep learning; Blockchain; Attention mechanism; Decentralised learning; Federated learning
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Horvath, Matus Vyrost, Tomas
【影响因子】9.848
【主题类别】
--
【Abstract】Since the Basel III accords, Expected Shortfall (ES) has become the recommended tail-risk measure in financial investments. Several methods of different theoretical backgrounds, complexity, and ease of implementation have since been developed for ES. As the competing set of models for ES grows, the question of which one to use becomes relevant to both academia and practitioners. We compare the predictive ability of four classes of models for ES estimation and identify a superior set. We verify the viability of these models in portfolio applications based on cryptocurrencies, an asset class with high volatility, particularly suitable for tail risk mitigation.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Expected shortfall; Forecasting; Cryptocurrency; Portfolio
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
CCF-A
【影响因子】9.235
【主题类别】
--
【Abstract】In response to the burgeoning cryptocurrency sector and its associated financial risks, there is a growing focus on detecting fraudulent activities and malicious addresses. Traditional studies are limited by their reliance on comprehensive historical data and address-wise manipulation, which are not available for early malice detection and fail to identify addresses controlled by the same fraudulent entity. We thus introduce Evolve Path Tracer, a novel solution designed for early malice detection in cryptocurrency. This system innovatively incorporates Asset Transfer Paths and corresponding path graphs in an evolve model, which effectively characterize rapidly evolving transaction patterns. First, for the target address, the Clustering-based Path Selector weight each Asset Transfer Path by finding sibling addresses along the Asset Transfer Paths. Evolve Path Encoder LSTM and Evolve Path Graph GCN then encode the asset transfer path and path graph within a dynamic structure. Additionally, our Hierarchical Survival Predictor efficiently scales to predict the address labels, demonstrating high scalability and efficiency. We rigorously tested Evolve Path Tracer on three real-world datasets of malicious addresses, where it consistently outperformed existing state-of-the-art methods. Our extensive scalability tests further confirmed the model's robust adaptability in dynamic prediction environments, highlighting its potential as a significant tool in the realm of cryptocurrency security.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Scalability; Predictive models; Encoding; Feature extraction; Adaptation models; Noise; Bitcoin; Phishing; Monitoring; History; Cryptocurrency; early detection; malicious behavior; transaction path
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【影响因子】8.373
【主题类别】
--
【Abstract】This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). Recently, UAVs have gained popularity as relay nodes to complement vehicles in IoV networks due to their ability to extend coverage through unbounded movement and superior communication capabilities. The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination processes are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for efficient and autonomous UAV coordination, and finally, a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the coordination between the selected UAVs is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). MDRL offers decentralized control and intelligent decision-making for the UAVs to maintain coverage and connectivity over the assigned vehicles. The evaluation results demonstrate that the proposed selection mechanism improves the stability of the selected relays, while MDRL maximizes the coverage and connectivity achieved by the UAVs. Both methods show superior performance compared to several benchmarks.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Internet of vehicles; Unmanned aerial vehicles; Multi-agent deep reinforcement learning; Blockchain; UAV coordination
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Shang, Jiaze Lu, Tianbo Cai, Yingjie Li, Yanfang
CCF-C
【影响因子】7.574
【主题类别】
--
【Abstract】The Raft consensus algorithm is based on the design of the leader, which simplifies the replication of logs and node changes. Unfortunately, the heavy responsibility of system interaction, including receiving requests from clients, transmitting heartbeats and entries, falls solely on the leader. A design with a strong leader can lead to an imbalance in the workload of nodes, thereby causing limited scalability. Additionally, the replication of a sole entry imposes constraints on the throughput. To alleviate these bottlenecks, we put forward anew solution, DRaft, which employs a double-layer architecture and multi-entry replication. To enable DRaft, we revamp the leader change mechanism by introducing Fi-leader and Se-leaders. Moreover, we incorporate a cache-buffer module into DRaft to enable concurrent entry replication. We present a theoretical framework composed of the CPF and CNF models to analyze the consensus success probability of DRaft. We expand DRaft to multi-layer Raft, and discover that the relationship between communication complexity and the number of nodes is proportional. Finally, we implement and evaluate DRaft, comparing its throughput and latency with those of BRaft and Engraft. We show that when 12K TPS is achieved, the latency of BRaft is twice that of DRaft.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Raft; Blockchain; Consensus mechanism; Leader election; Entry replication
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【影响因子】7.298
【主题类别】
--
【Abstract】This article presents MDST-GNN, a multi-distance spatial-temporal graph neural network for blockchain anomaly detection. To address challenges in detecting fraudulent cryptocurrency transactions, MDST-GNN integrates a multi-distance graph convolutional architecture with adaptive temporal modeling, enabling capture of both local and global spatial dependencies while inferring patterns from anonymized temporal data. The model incorporates self-supervised learning to enhance generalization ability. Experiments on the Elliptic dataset demonstrate MDST-GNN's superior performance over state-of-the-art methods, achieving improvements of 1.5% in AUC-ROC and 2.9% in AUC-PR. The model's robustness to temporal granularity and effectiveness in identifying suspicious transactions underscore its practical value for blockchain forensics.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】anomaly detection; blockchain; financial security; graph neural networks; temporal modeling
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【DOI】 10.1002/aisy.202400898
CCF-B
【影响因子】5.493
【主题类别】
--
【Abstract】Blockchain technology faces significant scalability challenges, characterized by low throughput and high transaction fees. Off-chain payment channel networks offer a promising solution by enabling faster transaction processing by offloading transactions away from the main blockchain. While existing research has primarily focused on enhancing instantaneous throughput, it often overlooks the critical issue of fund distribution imbalance at either end of the channel following transactions. This imbalance can negatively impact subsequent transactions, leading to reduced long-term throughput. Furthermore, temporary insufficiencies in channel balances may cause transaction requests to fail, further hindering overall payment channel network (PCN) performance. To address these limitations, this paper introduces an adaptive and efficient routing scheme AERO that leverages a balance coefficient to assess fund availability within channels. AERO facilitates optimal transaction path selection while incorporating probabilistic measures to evaluate channel transaction capacity, ensuring adaptive routing with minimal transaction losses and enhancements. Additionally, the proposed transaction scheduling algorithm in AERO incorporates a waiting queue at the transaction node, executing transactions only when the channel's capacity meets predefined requirements. Simulation results show that under the same network environment, AERO effectively maintains a throughput of approximately 70 even as transaction volumes rapidly increase. Moreover, AERO demonstrates notable cost efficiency, with transaction fees exceeding those of competing schemes by at least 5% in the Lightning topology and 25% in the Ripple topology.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchain; Payment channel network; Routing
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Liu, Zihao Wu, Huaping
【影响因子】5.111
【主题类别】
--
【Abstract】Accounting transactions and business data stored in blockchains are secure and resistant to tampering. Nonetheless, maintaining consistency between actual on- and off-chain data in blockchain auditing continues to be a significant challenge. We propose a dynamic validation model for blockchain data to address this challenge. Specifically, we adjusted the parameter selection rules in the traditional Rivest-Shamir-Adleman algorithm to assess the consistency between on-chain and off-chain factual data. We also incorporated random numbers into the Merkle Hash Tree algorithm to verify the equivalency of on- and off-chain actual data while maintaining quantity consistency. The model improves the integrity and authenticity of the data used in auditing and increases the credibility of the audit environment. Our study contributes significantly to the limited literature on using blockchain technology in auditing.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchain; Consistency verification; RSA encryption; N-MHT algorithm; Data credibility
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Yang, Zhexuan Qu, Xiao Chen, Zeng Sun, Guozi
CCF-C
【影响因子】5.047
【主题类别】
--
【Abstract】In cross-institutional user authentication, users' personal privacy information is often exposed to the risk of disclosure and abuse. Users should have the right to decide on their own data, and others should not be able to use user data without users' permission. In this study, we adopted a user-centered framework, so that users can obtain authorization among different resource owners through qualification proof, avoiding the dissemination of users' personal privacy data. We have developed a blockchain-based cross-institutional authorization architecture where users can obtain identity authentication between different entities by structuring transactions. Through the selective disclosure algorithm, the user's private information is hidden during the user identity authentication, and the authenticity of the user's private information is verified by disclosing the user's non-private information and authentication credentials. The architecture supports the generation of identity credentials of constant size based on atomic properties. We prototype the system on Ethereum. The prototype of the system is tested. The experiment proves that the sum of user information processing and verification time is about 80ms, and the time fluctuation of user information processing is very small. The results show that our data flow scheme can effectively avoid the privacy leakage problem in the user cross-agency authentication scenario with a small cost.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchain; Networking; Selective disclosure; Privacy-preserving; Data sharing
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Alrayes, Fatma S. Maray, Mohammed Alshuhail, Asma Almustafa, Khaled Mohamad Darem, Abdulbasit A. Al-Sharafi, Ali M. Alotaibi, Shoayee Dlaim
【影响因子】4.996
【主题类别】
--
【Abstract】In the present digital scenario, the explosion of Internet of Things (IoT) devices makes massive volumes of high-dimensional data, presenting significant data and privacy security challenges. As IoT networks enlarge, certifying sensitive data privacy while still employing data analytics authority is vital. In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications. Privacy-preserving machine learning (ML) training in the development of aggregation permits a demander to firmly train ML techniques with the delicate data of IoT collected from IoT devices. The current solution is primarily server-assisted and fails to address collusion attacks among servers or data owners. Additionally, it needs to adequately account for the complex dynamics of the IoT environment. In a large-sized big data environment, privacy protection challenges are additionally enlarged. The data dimensional can have vague meaningful patterns, making it challenging to certify that privacy-preserving models do not destroy the efficacy and accuracy of statistical methods. This manuscript presents a Privacy-Preserving Statistical Learning with an Optimization Algorithm for a High-Dimensional Big Data Environment (PPSLOA-HDBDE) approach. The primary purpose of the PPSLOA-HDBDE approach is to utilize advanced optimization and ensemble techniques to ensure data confidentiality while maintaining analytical efficacy. In the primary stage, the linear scaling normalization (LSN) method scales the input data. Besides, the sand cat swarm optimizer (SCSO)-based feature selection (FS) process is employed to decrease the high dimensionality problem. Moreover, the recognition of intrusion detection takes place by using an ensemble of temporal convolutional network (TCN), multi-layer auto-encoder (MAE), and extreme gradient boosting (XGBoost) models. Lastly, the hyperparameter tuning of the three models is accomplished by utilizing an improved marine predator algorithm (IMPA) method. An extensive range of experimentations is performed to improve the PPSLOA-HDBDE technique's performance, and the outcomes are examined under distinct measures. The performance validation of the PPSLOA-HDBDE technique illustrated a superior accuracy value of 99.49% over existing models.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Privacy-preserving; Ensemble model; Linear scaling normalization; High-dimensional; Big data; Intrusion detection
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Markou, Ioannis Sinnott, Derek Thomas, Ken
【影响因子】4.934
【主题类别】
--
【Abstract】Material Passports (MPs) have emerged as a crucial tool for promoting circularity and resource efficiency within the built environment, especially in light of the sector's substantial environmental impact. They can enable better material tracking, resource recovery, and waste reduction throughout the lifecycle of a building. However, their widespread implementation faces several limitations that hinder their full potential. This study presents a Systematic Literature Review (SRL) aimed to explore the methodologies currently used to create MPs. Guided by Kitchenham's [37] guidelines and enhanced by modified PRISMA processes, the SLR was conducted in three stages: Planning, Review Execution, and Reporting. Key steps included formulating research questions, defining search strategies (Scopus and Web of Science), and applying inclusion/exclusion criteria. An initial search yielded 549 papers (2019 -2024 ), reduced to 33 after rigorous screening. Snowballing approach used to identify relevant studies that might have not been included through the selection process. The final number of studies, that were included for further analysis was 40 . Thematic analysis identified 6 approaches of creating MPs, which were identified to be BIM-based, blockchain-based, platform-based, QR code/ RFID-based, and Level (s)-based approaches. Additionally, some studies identified key data requirements that will be included in Material Passports. Content analysis helped identified the benefits of using Material Passports and gap analysis highlighted the technological challenges and research gaps. The study identified several critical gaps, such as the static nature of MPs, which require substantial manual input and do not provide real-time updates to reflect changes in a building's lifecycle. These limitations affect their ability to accurately track materials and maintain data integrity throughout the building's operational life. Additionally, a gap in the existing literature is
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Material Passports; Circular Economy; Sustainability; Built Environment; Sustainable Architecture; Digital Technologies; BIM
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Cheng, Ching-Hsue Yang, Jun-He Dai, Jia-Pei
【影响因子】4.890
【主题类别】
--
【Abstract】Forecasting cryptocurrency prices is challenging due to market volatility and dynamic behavior. This study aims to enhance prediction accuracy for Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC) by proposing a novel deep learning framework. The framework integrates the Sparrow Search Algorithm (SSA) for selecting optimal technical indicators with Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Technical indicators derived from historical market data, including prices and trading volume, are analyzed to improve forecasting. The results demonstrate that the proposed framework effectively enhances prediction accuracy for BTC and LTC. For ETH, the best performance is achieved using all 34 indicators with the Bi-LSTM model. These findings highlight the importance of selecting relevant indicators and demonstrate the potential of advanced deep learning models in addressing the complexities of cryptocurrency markets. This research provides valuable insights and a reliable framework for improving cryptocurrency price predictions.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Cryptocurrency Price prediction; Technical Indicator selection; Sparrow search algorithm (SSA); Bidirectional long short-term memory (bi-LSTM); Time series forecasting; Deep learning
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【影响因子】4.715
【主题类别】
--
【Abstract】The healthy development of cotton industry is of great significance to the economy of Xinjiang, and the effective management of pests and diseases is the key to ensure the stable development of cotton industry. How to improve the efficiency of cotton pest and disease model detection and get better training effect is a key issue in the task of cotton pest and disease management. Based on the incremental detection model, this article combines the UAV and blockchain sharding technology to create a new cotton pest and disease detection framework, UAV-IFOD-shard. First, the backbone network of YOLOv5n is replaced with ShuffleNetV2, and the squeeze and excitation module is introduced to maintain accuracy and speed. Optimize the neck network using deeply separable convolution to reduce parameters and computation. Improve path aggregation network fusion by replacing concatenation with additive fusion to reduce the number of parameters. Then, an incremental learning method based on knowledge distillation for cotton pest and disease targets is proposed on the basis of the lightweight model to realize parameter updating and memory retention for new and old targets. In addition, the blockchain is further partitioned and a reputation evaluation mechanism is added to the process of federated learning model aggregation to optimize the whole federated learning process. Finally, pest and disease images were collected from cotton fields in several surrounding areas by UAV to construct a dataset on which distributed federation learning was trained. The experimental results show that our model achieves better results than some existing methods, with a reduction of about 69.95% in model parameters, 60% in training time, and only a loss of 5.7% in accuracy. The UAV-IFOD- shard framework improves the system throughput of federated learning and the quality of the aggregated model, and also shows better performance in the face of malicious node attacks, and it is a good choice to use this framework for cotton pest and disease detection in Xinjiang.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Diseases; Cotton; Data models; Training; Accuracy; Federated learning; Blockchains; Data privacy; Sharding; Object detection; Blockchain sharding; cotton disease detection; federated learning; incremental detection model; UAV images
【发表时间】2024
【收录时间】2025-04-10
【文献类型】
【Author】 Sun, Jin Song, Nana Wang, Lu Ye, Kexin Kang, Mengna
【影响因子】3.721
【主题类别】
--
【Abstract】In the cloud storage environment, data providers encrypt their data before transferring it to the cloud server to reduce storage pressure and facilitate internal sharing. However, most of the plans have certain drawbacks, such as: the search efficiency is low, the cloud server central problem is serious, and the ability to resist keyword guess attack is poor et al. To overcome these shortcomings, this paper presents a blockchain-based multi-keyword rank search scheme for B+ tree inverted index to improve the search accuracy and efficiency. First, we choose the top-k keywords with high weight to build a B+ tree inverted index. Then, the cloud server calculates the relevance score of ciphertext using the optimized TF-IDF formula and sends the top-k ciphertexts to the user. In addition, verification and match contract on the blockchain to verify the identity of users and address the semi-trust and centralization of cloud server. We have deployed reward and punishment contracts on the blockchain to regulate the behavior of data providers and cloud server. Rigorous security certification shows that the scheme can resist choose keyword attack (CKA) and keyword guess attack (KGA). Through experiments, it is found that our scheme has good search efficiency and communication efficiency.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Multi-keyword sorted search; B plus tree inverted index; Keyword weight; Relevance score calculation; Blockchain
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【影响因子】3.557
【主题类别】
--
【Abstract】With the escalation in the demand for privacy-preserving and tamper-resistant data management and processing on the public cloud, an increasing number of mainstream databases start to provide always-encrypted and blockchain-like features, including Microsoft SQL Server, MongoDB, and Alibaba PolarDB. The recent progress in Trusted Execution Environment (TEE) technology has enabled the deployment of the complete database engine within TEE. This implementation ensures that data stored in memory, cache, and registers is encrypted, thereby maintaining the confidentiality of information. In this paper, we present SecuDB, a multi-granularity privacy-preserving and tamper-resistant relational database by placing the entire RDBMS in Intel TDX. We propose a novel visibility control mechanism incorporating column masking, log masking, and statistics masking to realize fine-grained privacy preservation and devise an isolated TEE-endorsed temporal table method to support efficient data and query verifiability, without affecting insertion and selection performance. We evaluate SecuDB using Sysbench, TPC-C and TikTok copyright workloads. The results show that compared with a system without an enclave, SecuDB hits 84.7% and 94.7% of the performance when providing coarse-grained and fine-grained privacy preservation, respectively. While the overhead for tamper-resistance is less than 22.6%.
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】2025-04-10
【文献类型】
【DOI】 10.14778/3685800.3685815
【Author】 Chang, Le Yang, Xinyan Zhang, Kai Sui, Zhimei Zhao, Jian
CCF-C
【影响因子】3.488
【主题类别】
--
【Abstract】In the Industrial Internet of Things (IIoT), the data integrity in secure transmission improves the reliability of data analysis. To further reduce bandwidth consumption, the aggregate signature (AS) methodology is widely deployed in IIoT. Nevertheless, existing solutions are infeasible for digital twin empowered IIoT due to the following limitations: (i) the inconsistent time state of the data; and (ii) the ignored addition/removal issues of devices. Therefore, we propose a blockchain-based synchronized data transmission protocol (BT-SDT), which provides a synchronized clock for digital twin and implements dynamic device management. Specifically, we deploy the chaincode to maintain a clock synchronization service for AS between virtual space and physical space and re-design the chameleon hash modular on the redactable blockchain to update device information. We implement state-of-the-art solutions and BT-SDT to evaluate their computational and communication cost under a real IIoT dataset in Raspberry Pi and blockchain, which confirms the practical feasibility of BT-SDT.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Industrial IoT; Digital twin; Data transmission; Synchronized aggregate signature; Blockchain
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【影响因子】3.476
【主题类别】
--
【Abstract】Signatures are crucial in blockchain-based Decentralised Finance (DeFi) protocols because they ensure the security and integrity of transactions and smart contracts. Due to the weakness in the signature scheme, it is possible to carry out a malleability attack (MA) by changing the transaction ID (TxId) without affecting the transaction's actual content or validity. Currently, signature systems can only resist partial malleability attacks from multiple attack paths. This paper proposes an advanced multi-signature scheme (MSS) as a supplementary signature that integrates unmalleable transaction implementations. In MSS, the owners and block producers themselves generate the signature. MSS improves transaction efficiency by allowing many signers to establish a joint signature, which has piqued interest. Despite the method's complexity and time-consuming nature, this research has adapted MSS to the blockchain to safeguard against malleability attacks. By integrating it with several other optimisations, such as executing intermediate transactions using a hash function, MSS ensures complete resistance against malleability attacks. In comparison to baseline approaches, testbed simulations demonstrate scalability and 15% higher resistance against malleability attack success.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Protocols; Security; Resistance; Fuses; Bitcoin; Scalability; Aggregates; Real-time systems; Blockchain; digital signatures; multi-signatures; malleability attacks; multi-signatures; malleability attacks
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【影响因子】3.476
【主题类别】
--
【Abstract】In the past three decades, there has been a sweeping trend in Western and developed countries worldwide to transform the vertically integrated electricity supply chain into competitive electricity markets to diversify investment in the system and ultimately drive down operation costs. Nonetheless, due to some geopolitical and economic reasons, many developing countries adopted a modestly liberalized version of the power market (imperfect market). With the trend of privatization, specifically at the generation level, to leverage the hypothetical competitiveness, countries that did not adopt a full-fledged market structure face a dilemma. The system operators of incumbent imperfect market models find it increasingly difficult to deal with multiple private ownership of Independent Power Producers who are unwilling to share their detailed operational parameters for long-term generation scheduling (lasting for years). In this paper, Blockchain (BC) is being advocated as a platform that simulates a virtual market environment to address such issues. The proposed BC-based structure allows generators to participate in the short-term scheduling mechanism (such as day-ahead) in a trust-free environment without sharing their vital data yet achieving efficient, market-grade solutions. The feasibility of this new proposition is demonstrated through three different application scenarios, utilizing real-world load and renewable generation profiles sourced from the respective Grid System Operators databases. Python library (PYPSA) and Ethereum Testnet are being used for grid simulation and BC platform implementation respectively. The results of BC-assisted generation scheduling are presented and compared with the imperfect market model to highlight the viability of the proposed new approach.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Job shop scheduling; Costs; Renewable energy sources; Optimization; Mathematical models; Blockchains; Generators; Load modeling; Industries; Dynamic scheduling; Blockchain; decentralization; energy markets; generation-scheduling
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Song, Xing-Shuo Li, Shi-Yong
【影响因子】3.476
【主题类别】
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【Abstract】The Direct Acyclic Graph (DAG)-based blockchain offers impressive scalability and finds applications across various sectors, notably the Internet of Things (IoT) industry. Within this domain, IOTA has been purpose-built for IoT. This article analyzes the performance of IOTA's data structure Tangle based on the Markovian arrival process (MAP). Meanwhile, we have considered this situation in a newly issued transaction and selected multiple tips for validation. Firstly, we show that this Markov process is a level-dependent quasi-birth-death (QBD) process. Then, we prove that the QBD process is irreducible, positive, and recurrent. Furthermore, smooth probability vectors for the QBD process are given, and the performance of the DAG-based blockchain system is analyzed. Moreover, we verify the validity of our theoretical results with numerical examples, pointing out how some key system parameters affect the performance metrics of this system. Therefore, we hope that the methodology and results presented in this paper will shed some light on DAG-based blockchain systems so that a series of promising research projects can be developed.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchains; Markov processes; Delays; Scalability; Security; Internet of Things; Vectors; Analytical models; Performance analysis; Memory; Internet of Things (IoT); direct acyclic graph (DAG); IOTA; QBD process; Markovian arrival process (MAP)
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Moriyama, Koichi Otsuka, Akira
【影响因子】3.476
【主题类别】
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【Abstract】Increasing attention to digital identity and self-sovereign identity (SSI) is gaining momentum. SSI brings various benefits to natural persons, such as owning controls; conversely, digital identity systems in the real world require Sybil-resistance to comply with anti-money laundering (AML) and other needs. CanDID by Maram et al. proposed that decentralized digital identity systems may achieve Sybil-resistance and preserve privacy by utilizing multi-party computation (MPC), assuming a distributed committee of trusted nodes. Pass et al. proposed the formal abstraction of attested execution secure processors (AESPs) while equipping hardware-assisted security in mobile devices has become the norm. We first describe our proposal to utilize AESPs for building secure Sybil-resistant SSI systems, the architecture with a set of system protocols Pi(Gatt), which brings drastic flexibility and efficiency compared to existing systems. In addition, we propose a novel scheme that enables users (holders) to request verifiers to verify their credentials without AESPs, and it further achieves unlinkability among credentials created for public verification. Our scheme introduces a simplified format for computed claims and commitment-based anonymous identifiers. We also describe a technique to utilize zero-knowledge membership proofs, in particular, "One-Out-of-Many Proofs" Sigma -protocol by Groth and Kohlweiss, which can prove the existence of an expected credential without identifying it. Along with other techniques, such as utilizing the BBS+ signature scheme, we demonstrate how our scheme can achieve its goals with the extended anonymous and Sybil-resistant SSI system protocols Pi(Gatt) . Entitling unlinkability among derived credentials in the anonymous Sybil-resistant SSI results in proper privacy preservation.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Security; Protocols; Program processors; Blockchains; Privacy; Mobile applications; Multi-party computation; Electrical resistance measurement; Buildings; W3C; Attested execution secure processors; decentralized digital identity; permissionless blockchain; self-sovereign identity; Sybil-resistant; verifiable credentials; zero-knowledge membership proofs Sigma-protocol
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Aleisa, Mohammed A.
【影响因子】3.476
【主题类别】
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【Abstract】This research introduced a new novel "Unified Quantum-Resilient Blockchain-Zero-Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF)" to upgrade the IoT environments with greater security. To enable privacy-preserving authentication, access control, and secure communication, the framework integrates blockchain technology with Zero Trust Architecture (ZTA) and post-quantum cryptography. A hybrid Reinforcement-Lattice Blockchain KeyGen for quantum-resilient key generation, Deep Q-Network Multi-Factor Secure Key (DQN-MFSK) for dynamic selection of keys, and Zero-Knowledge Proof for privacy-preserving signatures are employed to achieve secure IoT settings. This architecture entails data privacy and confidentiality, auditability and traceability, and withstanding evolving threats, including potential threats in terms of quantum attacks. It then uses blockchain technology for recording unalterable data of identity and access management while Zero-Knowledge Proofs (ZKP) ensures authentication and verification without revealing sensitive information. By decentralizing identity management and enabling multi-factor authentication, QBC-ZKPAF provides robust security and privacy solutions for IoT networks. The experimental results demonstrate the model's effectiveness with 98% privacy preservation, 700 TPS throughput, 0.7 J energy consumption, 0.98 quantum resilience, and 96% access control effectiveness, making it highly suitable for modern IoT and blockchain applications.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchains; Security; Authentication; Privacy; Internet of Things; Cryptography; Computer architecture; Access control; Resilience; Data privacy; IoT security; privacy preservation; access control; blockchain technology; zero trust architecture; quantum resilience; data confidentiality; user authentication; decentralization; secure communication
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【影响因子】3.310
【主题类别】
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【Abstract】With the growth of cloud computing and the popularity of electronic health records (EHR), more and more patients and hospitals are uploading EHR to the cloud for storage, retrieval and organization. Due to the privacy of EHR, cloud-based EHR systems need to protect data security and provide access control, and attribute-based encryption (ABE) is the appropriate technology. Nevertheless, traditional single-center ABE schemes do not conform to the collaborative scenario of electronic health care, and some of them do not support real-time attribute update. Consequently, this paper proposes a lightweight CP-ABE scheme for EHR over cloud based on blockchain and secure multi-party computation (LCBS). First, we introduce the model of multi-authority and innovatively apply secure multi-party computation to initialize the system, which maintains normal system operation while the power is decentralized. Second, we deploy a blockchain suitable for EHR systems to record the users' key information, assisting multiple entities to verify the key at different stages and protecting the EHR from illegal acquisition. In addition, our scheme supports lightweight attribute update, which requires small amount of computational overhead to achieve instant attribute update. Finally, through formal security analysis and simulation experiments of the LCBS system, it is shown that our scheme guarantees data security and improves computing efficiency.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】blockchain; CP-ABE; electronic health record; secret sharing; secure multi-party computation
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【DOI】 10.1002/ett.70053
【Author】 Zhang, Xinlin Qiu, Fangdao Liu, Jibin
【影响因子】3.101
【主题类别】
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【Abstract】Climate change caused by carbon emissions is a hot topic of concern. Enhancing carbon emission performance (CEP) emerges as a pivotal strategy to curtail carbon emissions, with the digital economy recognized as a crucial instrument for bolstering CEP. Grounded in theoretical analysis, this article takes the Yangtze River Delta region (YRD) as the research object and conducts empirical analysis for the period from 2010 to 2021. The Super Epsilon-Based Measure (EBM) model was employed to assess CEP, while the entropy method was used to quantify the level of the digital economy. Baseline regression models and mediation effect models were constructed to test the research hypotheses. Additionally, the Spatial Durbin Model (SDM) was utilized to analyze the spatial spillover effects of the digital economy. Some conclusions were drawn as follows. Firstly, both the digital economy and CEP exhibit growing trends and demonstrate significant spatial distribution characteristics. Cities with high CEP are increasingly concentrated along the Yangtze River and coastal areas. Meanwhile, the digital economy generally demonstrates a spatial distribution pattern of being higher in the southeast and lower in the northwest. Secondly, the digital economy exerts a notable and consistent positive influence on CEP, but this impact is not primarily achieved through promoting green technology innovation. Instead, the digital economy exhibits a stronger intermediary effect on CEP by facilitating industrial structure upgrading and rationalization. Thirdly, the digital economy significantly enhancing local CEP but having an insignificant impact on neighboring cities' CEP. To address these findings, cities ought to invest in digital infrastructure, incentivize digital innovation through policy and financial backing, and harness advanced technologies like 5G and blockchain to promote low-carbon, intelligent production and lifestyles, while enhancing industrial structure and regional cooperation to foster a low-carbon digital economy network.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】digital economy; carbon emission performance (CEP); industrial structure; spatial spillover effect; the Yangtze River Delta (YRD), China
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Matani, Alemeh Sahafi, Amir Broumandnia, Ali
【影响因子】2.420
【主题类别】
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【Abstract】Scalability is a challenging issue in blockchain technology, becoming more critical as blockchain systems grow in the amount of transaction data or number of users. Some key metrics affecting scalability in blockchain technology include transaction throughput and latency as well as the storage and communication capabilities the nodes need to participate in the network. Most proposed solutions for blockchain scalability handle only one or a few of these scalability issues assuming that all the blockchain nodes are homogeneous in computational, communication, and storage capabilities. This assumption results in degrading system efficiency and worsens scalability issues due to the unfair distribution of workload among the blockchain nodes. This paper presents a new solution for blockchain scalability that enhances scalability using multi-level sharding based on the heterogeneity of network nodes. In fact, the heterogeneous nodes are dynamically scored based on their resource capacities and contributions to the protocol and then are organized into multiple levels based on their calculated scores and form a hierarchical tree structure. Additionally, a new mechanism for consensus is introduced which makes use of the hierarchical structure to enable a more secure and decentralized consensus-making process. The simulation experiments showed that the proposed solution significantly improves various scalability measurements, including throughput, latency, storage efficiency, and communication overhead while increasing decentralization and maintaining security. The improvement rate over the scalability metrics achieved by the proposed work is proportional to the characteristics of the hierarchical tree structure such as depth and branching ratio.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Blockchain; Scalability; Sharding; Consensus; Hierarchical; Heterogeneity
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Zhang, Wei Cai, Jun
【影响因子】1.140
【主题类别】
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【Abstract】Aiming at the dynamic demand path optimization problem of cold chain distribution, this paper combines the technical advantages of blockchain with the characteristics of cold chain path optimization problem on the basis of existing literature research. A multi-objective path optimization model with time window considering customer satisfaction is established, and then an example is designed and genetic algorithm (GA) is used to solve the above model. By comparing the indicators before and after optimization, it is concluded that the distribution cost of the distribution optimization scheme is low. Therefore, the combination of blockchain technology and cold chain distribution can effectively improve distribution efficiency, thereby reducing transportation costs and improving customer satisfaction. Finally, we completed the following work: 1) Summarized the research literature on vehicle routing problem (VRP) and cold chain logistics, and pointed out the current research trend. 2) The mathematical modeling of the actual logistics distribution problem is studied. Through the study of vehicle routing problem with time window (VRPTW) modeling method and multi-objective optimization theory, a multi-objective function is established and unified into cost objectives. The modeling elements are determined, and the multi-objective vehicle routing optimization model with time window is established by combining the objective function. Then the GA is determined as the algorithm for solving the model in this paper, and the algorithm flow is designed. 3) The algorithm parameters are set according to the actual distribution data of an enterprise, and then the effectiveness and optimization of the algorithm are tested to check the mutual constraints between the constraints of the model, as well as the optimization of the target value of the algorithm to solve the problem. The obtained path optimization scheme has more advantages than the original scheme.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Cold chain; Path optimization; Blockchain technology; Time window
【发表时间】2025
【收录时间】2025-04-10
【文献类型】
【Author】 Cao, Jie
【影响因子】1.140
【主题类别】
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【Abstract】In the era of digital economy, trade data and goods transaction data in the field of international trade have become increasingly important. In the digital era, the transfer of international trade-related data from the physical world to the digital world through the application of advanced Blockchain Technology (BT) is an important means to promote the development of international trade in the digital era. This paper aimed to analyze Cross- Border E-Commerce (CBEC) economic and trade Data Management (DM) devices based on BT. This paper discussed the application of blockchain in CBEC trade, and proposed a blockchain security algorithm. Based on this, an experimental analysis of CBEC economy and trade DM was carried out. The experimental results of this paper showed that in the logistics costs of CBEC companies, the logistics costs of CBEC companies under the trade DM device based on BT accounted for 20%-30% of the total costs. The logistics cost of CBEC companies under the trade DM device based on traditional technology accounted for 30%-40% of the total cost; in the comparison of the favorable rate of CBEC companies' products, the favorable rate of Company A's products under the trade DM device based on BT was 98.2%, and that of Company B was 98.6%. Under the trade DM device based on traditional technology, the favorable rate of Company C's products was 90.8%, and that of Company D was 91.1%. To sum up, based on the BT trade DM, the company's logistics costs would be greatly reduced and its product quality control was in place.
你可以尝试使用大模型来生成摘要 立即生成
【Keywords】Cross-border E-commerce; Blockchain technology; Trade data; Management devices
【发表时间】2025
【收录时间】2025-04-10
【文献类型】