Search Results (316)

With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. How to achieve fine-grained access control and privacy protection for massive devices while ensuring secure and reliable data circulation has become a key issue that needs to be urgently addressed in the current IoT field. To address the above challenges, this paper proposes a blockchain-based data transaction and privacy protection framework. First, the framework builds a multi-layer security architecture that integrates blockchain and IPFS and adapts to the “end–edge–cloud” collaborative characteristics of IoT. Secondly, a data sharing mechanism that takes into account both access control and interest balance is designed. On the one hand, the mechanism uses attribute-based encryption (ABE) technology to achieve dynamic and fine-grained access control for massive heterogeneous IoT devices; on the other hand, it introduces a game theory-driven dynamic pricing model to effectively balance the interests of both data supply and demand. Finally, in response to the needs of confidential analysis of IoT data, a secure computing scheme based on CKKS fully homomorphic encryption is proposed, which supports efficient statistical analysis of encrypted sensor data without leaking privacy. Security analysis and experimental results show that this scheme is secure under standard cryptographic assumptions and can effectively resist common attacks in the IoT environment. Prototype system testing verifies the functional completeness and performance feasibility of the scheme, providing a complete and effective technical solution to address the challenges of data integrity, verifiable transactions, and fine-grained access control, while mitigating the reliance on a trusted central authority in IoT data sharing. Full article

The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection. Full article

This study explores the transformative potential of blockchain technology in overhauling conventional housing rental systems. It specifically addresses persistent issues, such as information asymmetry, fraudulent listings, weak Rental Agreements, and data breaches. A comprehensive review of ten academic publications highlights the architectural frameworks, underlying technologies, and myriad benefits of decentralized rental platforms. The intrinsic characteristics of blockchain—immutability, transparency, and decentralization—are pivotal in enhancing the credibility of rental information and proactively preventing fraudulent activities. Smart contracts emerge as a key innovation, enabling the automated execution of Rental Agreements, thereby significantly boosting efficiency and minimizing reliance on intermediaries. Furthermore, Decentralized Identity (DID) solutions offer a robust mechanism for securely managing identities, effectively mitigating risks associated with data leakage, and fostering a more trustworthy environment. The suitability of platforms such as Hyperledger Fabric for developing such sophisticated rental systems is also critically evaluated. Blockchain-based systems promise to dramatically increase market transparency, bolster transaction security, and enhance fraud prevention. They also offer streamlined processes for dispute resolution. Despite these significant advantages, the widespread adoption of blockchain in the rental sector faces several challenges. These include inherent technological complexity, adoption barriers, the need for extensive legal and regulatory adaptation, and critical privacy concerns (e.g., ensuring compliance with GDPR). Furthermore, blockchain scalability limitations and the intricate balance between data immutability and the necessity for occasional data corrections present considerable hurdles. Future research should focus on developing user-friendly DID solutions, enhancing blockchain performance and cost-efficiency, strengthening smart contract security, optimizing the overall user experience, and exploring seamless integration with emerging technologies. While current challenges are undeniable, blockchain technology offers a powerful suite of tools for fundamentally improving the rental market’s efficiency, transparency, and security, exhibiting significant potential to reshape the entire rental ecosystem. Full article

The collaborative construction of large-scale, diverse datasets is crucial for developing high-performance machine learning models. However, this collaboration faces significant challenges, including ensuring data security, protecting participant privacy, maintaining high dataset quality, and aligning economic incentives among multiple stakeholders. Effective risk management strategies are essential to systematically identify, assess, and mitigate potential risks associated with data collaboration. This study proposes a federated blockchain-based framework designed to manage multiparty dataset collaborations securely and transparently, explicitly incorporating comprehensive risk management practices. The proposed framework involves six core entities—key distribution center (KDC), researcher (RA), data owner (DO), consortium blockchain, dataset evaluation platform, and the orchestrating model itself—to ensure secure, privacy-preserving and high-quality dataset collaboration. In addition, the framework uses blockchain technology to guarantee the traceability and immutability of data transactions, integrating token-based incentives to encourage data contributors to provide high-quality datasets. To systematically mitigate dataset quality risks, we introduced an innovative categorical dataset quality assessment method leveraging label reordering to robustly evaluate datasets. We validated this quality assessment approach using both publicly available (UCI) and privately constructed datasets. Furthermore, our research implemented the proposed blockchain-based management system within a consortium blockchain infrastructure, benchmarking its performance against existing methods to demonstrate enhanced security, reliability, risk mitigation effectiveness, and incentive alignment in dataset collaboration. Full article

Many hospitals still lack digital traceability in hygiene and cleaning management, leading to operational inefficiencies and inconsistent quality control. This study aims to establish cleaning and hygiene processes in healthcare services that are planned in accordance with standards, as well as to enhance the traceability and sustainability of these processes through digitalization. This study proposes a Hyperledger Fabric-based blockchain architecture to establish a reliable and transparent quality assurance system in process management. The proposed Quality Assurance Model utilizes digital technologies and IoT-based RFID devices to ensure the transparent and reliable monitoring of cleaning processes. Operational data related to cleaning processes are automatically recorded and secured using a decentralized blockchain infrastructure. The permissioned nature of Hyperledger Fabric provides a more secure solution compared to traditional data management systems in the healthcare sector while preserving data privacy. Additionally, the execute–order–validate mechanism supports effective data sharing among stakeholders, and consensus algorithms along with chaincode rules enhance the reliability of processes. A working prototype was implemented and validated using Hyperledger Caliper under resource-constrained cloud environments, confirming the system’s feasibility through over 100 TPS throughput and zero transaction failures. Through the proposed system, cleaning/hygiene processes in patient rooms are conducted securely, contributing to the improvement of quality standards in healthcare services. Full article

Trust evaluation has become a major challenge in the quickly developing Internet of Things (IoT) environment because of the vulnerabilities and security hazards associated with networked devices. To overcome these obstacles, this study offers a novel approach for evaluating trust that uses IOTA Tangle technology. By decentralizing the trust evaluation process, our approach reduces the risks related to centralized solutions, including privacy violations and single points of failure. To offer a thorough and reliable trust evaluation, this study combines direct and indirect trust measures. Moreover, we incorporate IOTA-based trust metrics to evaluate a node’s trust based on its activity in creating and validating IOTA transactions. The proposed framework ensures data integrity and secrecy by implementing immutable, secure storage for trust scores on IOTA. This ensures that no node transmits a wrong trust score for itself. The results show that the proposed scheme is efficient compared to recent literature, achieving up to +3.5% higher malicious node detection accuracy, up to 93% improvement in throughput, 40% reduction in energy consumption, and up to 24% lower end-to-end delay across various network sizes and adversarial conditions. Our contributions improve the scalability, security, and dependability of trust assessment processes in Internet of Things networks, providing a strong solution to the prevailing issues in current centralized trust models. Full article

Blockchain provides the opportunity for organizations to execute trustable collaborations through smart contract automations. However, linkability problems exist in blockchain-based collaboration platforms due to privacy leakages, which, when exploited, will result in tracing transaction patterns to users and exposing collaborating organizations and parties. Some privacy-preserving mechanisms have been adopted to reduce linkability problems through the integration of access control systems to smart contracts, off-chain data storage, usage of permissioned blockchain, etc. Still, linkability problems persist in applications deployed in both private and public blockchain networks. Zero-knowledge proof (ZKP) systems provide mechanisms for verifying the correctness of transactions and actions executed on the blockchain without revealing complete information about the transaction. Hence, ZKP systems provide a potential solution to eliminating linkability problems in blockchain-based collaboration systems. The objective of this paper is to identify various linkability problems that exist in blockchain-enabled collaboration systems and understand how ZKP algorithms and smart contract frameworks can be used in addressing the linkability problems. Furthermore, a proof of concept (PoC) is implemented and simulated to demonstrate a ZKP system for a privacy-preserving feedback mechanism that mitigates linkability problems in collaboration systems. The scenario-based results from the PoC evaluation show that a feedback system that includes project participants’ verification through membership proofs, verification of on-time submission of feedback through range proofs, and encrypted calculation of feedback scores through homomorphic arithmetic provides a privacy-aware system for executing collaborations on the blockchain without linking project participants. Full article

Off-chain payments in the Internet of Things (IoT) enhance the efficiency and scalability of blockchain transactions. However, existing privacy mechanisms face challenges, such as the disclosure of payment channels and transaction traceability. Additionally, the rise of quantum computing threatens traditional public key cryptography, making the development of post-quantum secure methods for privacy protection essential. This paper proposes a post-quantum ring signature scheme based on hash functions that can be applied to off-chain payments, enhancing both anonymity and linkability. The scheme is designed to resist quantum attacks through the use of hash-based signatures and to prevent double spending via its linkable properties. Furthermore, the paper introduces an improved Hash Time-Locked Contract (HTLC) that incorporates a Signature of Knowledge (SOK) to conceal the payment path and strengthen privacy protection. Security analysis and experimental evaluations demonstrate that the system strikes a favorable balance between privacy, computational efficiency, and security. Notably, the efficiency benefits of basic signature verification are particularly evident, offering new insights into privacy protection for post-quantum secure blockchain. Full article

This paper proposes a novel mixed game-based day-ahead operation strategy for multi-building integrated energy systems, which innovatively addresses both inter-building cooperation and non-cooperative energy transactions with system operators under uncertainties. Specifically, a bi-level operation model is established in which the upper level maximizes the benefits of the energy system operator, and the lower level minimizes the costs of multiple buildings. Then, in consideration of source-load uncertainties in multiple building energy systems, the fuzzy chance-constrained programming method is introduced, and the clear equivalent class method is used to reformulate the fuzzy chance constrained model into a tractable deterministic type. Further, a privacy-preserving hierarchical solution approach is presented to solve the bi-level optimization model, and the Shapley value method is adopted for benefits redistribution. Case studies on a multi-building system in East China showcase the effectiveness of the proposed work and demonstrate that the proposed strategy contributes to reducing the operation costs of the multi-building system by approximately 3.98% and increasing the revenue of the energy system operators by 10.31%. Full article

Increased digital banking operations have brought about a surge in suspicious activities, necessitating heightened real-time fraud detection systems. Conversely, traditional static approaches encounter challenges in maintaining privacy while adapting to new fraudulent trends. In this paper, we provide a unique approach to tackling those challenges by integrating VAE-QLSTM with Federated Learning (FL) in a semi-decentralized architecture, maintaining privacy alongside adapting to emerging malicious behaviors. The suggested architecture builds on the adeptness of VAE-QLSTM to capture meaningful representations of transactions, serving in abnormality detection. On the other hand, QLSTM combines quantum computational capability with temporal sequence modeling, seeking to give a rapid and scalable method for real-time malignancy detection. The designed approach was set up through TensorFlow Federated on two real-world datasets—notably IEEE-CIS and European cardholders—outperforming current strategies in terms of accuracy and sensitivity, achieving 94.5% and 91.3%, respectively. This proves the potential of merging VAE-QLSTM with FL to address fraud detection difficulties, ensuring privacy and scalability in advanced banking networks. Full article

Background: The increasing complexity of global supply chains has intensified the demand for transparency, traceability, security, and sustainability in logistics and operations. Blockchain technology enables decentralized, immutable frameworks that improve data integrity, automate transactions via smart contracts, and integrate seamlessly with the IoT and AI. Methods: This bibliometric review analyzes 559 peer-reviewed publications retrieved from Scopus and Web of Science using a PRISMA-guided protocol. Data were processed with Bibliometrix and Biblioshiny to examine scientific production, contributing institutions, author countries, collaboration patterns, thematic clusters, and keyword evolution. Results: The analysis reveals a 400% increase in publications after 2020, with China, India, and the USA leading in output but with limited international collaboration. Keyword co-occurrence and thematic mapping reveal dominant topics, including smart contracts, food supply chain traceability, and sustainability, as well as emerging themes such as decentralization, privacy, and the circular economy. Conclusions: The field is marked by interdisciplinary growth, yet it remains thematically and geographically fragmented. This review maps the intellectual structure of blockchain-enabled sustainable supply chains, offering insights for policymakers, developers, and industry leaders and outlining future research avenues centered on global cooperation, platform efficiency, and ethical and regulatory dimensions. Full article

An Identity Management System (IDMS) is responsible for managing and organizing identities and credentials exchanged between users, Identity Providers (IDPs), and Service Providers (SPs). The primary goal of IDMS is to ensure the confidentiality and privacy of users’ personal data. Traditional IDMS relies on a third party to store user information and authenticate the user. However, this approach poses threats to user privacy and increases the risk of single point of failure (SPOF), user tracking, and data unavailability. In contrast, decentralized IDMSs that use blockchain technology offer potential solutions to these issues as they offer powerful features including immutability, transparency, anonymity, and decentralization. Despite its advantages, blockchain technology also suffers from limitations related to performance, third-party control, weak authentication, and data leakages. Furthermore, some blockchain-based IDMSs still exhibit centralization issues, which can compromise user privacy and create SPOF risks. This study proposes a decentralized IDMS that leverages blockchain and smart contract technologies to address the shortcomings of traditional IDMSs. The proposed system also utilizes the Interplanetary file system (IPFS) to enhance the scalability and performance by reducing the on-chain storage load. Additionally, the proposed IDMS employs the Elliptic Curve Integrated Encryption Scheme (ECIES) to provide an extra layer of security to protect users’ sensitive information while improving the performance of the systems’ transactions. Security analysis and experimental results demonstrated that the proposed IDMS offers significant security and performance advantages compared to its counterparts. Full article

This research examined the domain of intelligent transportation systems (ITS) by analyzing the impact of scholarly work and thematic prevalence, as well as focusing attention on vehicles, their technologies, cybersecurity, and related scholarly technologies. This was performed by examining the scientific literature indexed in the Scopus database. This study analysed 2919 documents published between 2018 and 2025. The findings indicated that the highest and most significant journal was derived from IEEE Transactions on Vehicular Technology, with significant standing to the growth of communication and computing on vehicles with edge computing and AI optimization of vehicular systems. In addition, important PST research conferences highlighted the growing interest in academic research in cybersecurity for vehicle networks. Sensor networks, pose forensics, and privacy-preserving communication frameworks were some of the significant contributing fields marking the significance of the interdisciplinary nature of this research. Employing bibliometric analysis, the literature illustrated the multiple channels integrating knowledge creation and innovation in ITS through citation analysis. The outcome suggested an increasingly sophisticated research area, weighing technical progress and increasing concern about security and privacy measures. Further studies must investigate edge computing integrated with AI, advanced privacy-preserving linguistic protocols, and new vehicular network intrusion detection systems. Full article

Self-sovereign identity (SSI) is an emerging model for digital identity management that empowers individuals to control their credentials without reliance on centralized authorities. This work presents YouGovern, a blockchain-based SSI system deployed on Binance Smart Chain (BSC) and compliant with W3C Decentralized Identifier (DID) standards. The architecture includes smart contracts for access control, decentralized storage using the Inter Planetary File System (IPFS), and long-term persistence via Web3.Storage. YouGovern enables users to register, share, and revoke identities while preserving privacy and auditability. The system supports role-based permissions, verifiable claims, and cryptographic key rotation. Performance was evaluated using Ganache and Hardhat under controlled stress tests, measuring transaction latency, throughput, and gas efficiency. Results indicate an average DID registration latency of 0.94 s and a peak throughput of 12.5 transactions per second. Compared to existing SSI systems like Sovrin and uPort, YouGovern offers improved revocation handling, lower operational costs, and seamless integration with decentralized storage. The system is designed for portability and real-world deployment in academic, municipal, or governmental settings. Full article

The rapid development of renewable energy necessitates advanced solutions that address the volatility and complexity of modern power systems. This study proposes an AI-driven integrated optimization framework for a Virtual Power Plant (VPP) and Smart Grid, aiming to enhance renewable energy utilization, reduce grid losses, and improve economic dispatch efficiency. Leveraging deep reinforcement learning (DRL), this framework dynamically adapts to real-time grid conditions, optimizing multi-objective functions such as power loss minimization and renewable energy maximization. This research incorporates data-driven decision-making, blockchain for secure transactions, and transformer architectures for predictive analytics, ensuring its scalability and adaptability. Experimental validation using real-world data from the Shenzhen VPP demonstrates a 15% reduction in grid losses and a 22% increase in renewable energy utilization compared to traditional methods. This study addresses critical limitations in existing research, such as data rigidity and privacy risks, by introducing federated learning and anonymization techniques. By bridging theoretical innovation with practical application, this work contributes to the United Nations’ Sustainable Development Goals (SDGs) 7 and 13, offering a robust pathway toward a sustainable and intelligent energy future. The findings highlight the transformative potential of AI in power systems, providing actionable insights for policymakers and industry stakeholders. Full article