Abstract Federated learning (FL) is vulnerable to intermittent data poisoning and unreliable updates from untrusted participants. Existing blockchain-aided FL schemes provide immutable logging of model updates but still face three limitations: lack of temporal adversary modeling, reliance on costly on-chain computation, and communication overhead that restricts scalability. We propose State-Space Model-based Truth Discovery (SSMTD), a unified framework that addresses these challenges by integrating: (1) a Hidden Markov filter that assigns epoch-wise soft reputations to capture time-varying client reliability, (2) off-chain oracles that compute lightweight gradient-consistency metrics to avoid prohibitive gas costs, and (3) a decentralized oracle design that ensures robustness while maintaining sublinear communication growth as the number of clients increases. The inferred reputations are anchored on-chain. These reputations enable automated reward–penalty contracts that re-weight or exclude malicious participants in subsequent aggregation rounds. This combination of temporal reliability modeling, oracle-assisted off-chain computation, and scalable decentralized verification distinguishes SSMTD from prior approaches, which either neglect adversary dynamics or incur prohibitive overhead. Experiments on FMNIST, CIFAR-10, and LEAF under varying attack intensities and noise conditions show that SSMTD consistently outperforms reputation-based baselines, achieving 2.6–11.9 percentage-point improvements in F1-scores alongside simultaneous gains in precision and recall. System profiling further confirms stable throughput, sublinear latency growth, and minimal resource overhead, demonstrating strong scalability.
As a result of the conducted research, it has been revealed that the scientific and practical literature lacks sufficient studying of the functioning and systematisation of existing data on new financial technologies, on the basis of which modern financial services and products are developed. There-fore, the features of distributed ledger technology (hereinafter referred to as DLT) are chosen as the subject of research in this work. The purpose of the study is to analyse the application of the DLT in modern conditions based on exploring theoretical and practical aspects of the problem under study. In accordance with the set purpose, the objectives are to identify the features of the DLT, to develop a classification of this technology according to appropriate criteria as well as to identify the directions of regulating the use of the DLT in modern conditions. The sources of information are informational and analytical materials and empirical data from open sources of both public authorities and commercial organisations. In the course of the study, the features of the DLT are considered, the criteria for its classification are presented as well. Based on the results of the conducted study, it has been concluded that the features of the DLT described by us are fragmentary reflected in regulatory legal acts, which once again emphasises the need to develop common guidelines for public policy in relation to the legal and technical regulation of the DLT in Russia.
David Ojimaojo Ebiloma, Opeoluwa Akinradewo, Clinton Ohis Aigbavboa
et al.
Blockchain is a decentralised system based on cryptographic procedures and smart contracts that records every transaction and shares it with all parties involved, thereby handling issues of delays and trust. However, as identified in past studies, there are barriers to its adoption in the construction industry, especially in developing countries. There are no studies that assessed the impact of these barriers on the various procurement systems used in Nigeria; hence, this study is aimed at developing the statistical impact models of these barriers on critical procurement systems in Nigeria, with a view of identifying the most available and least-affected system that is best fit for blockchain adoption. The study adopted the quantitative research approach through a questionnaire survey. Data was collected from 182 stakeholders in the Nigerian construction industry through a snowball sampling technique; these stakeholders were grouped as clients, contractors and consultants. The retrieved data were analysed using descriptive statistics and linear regression, while Cronbach’s alpha was used to test for reliability. The R2 and the significance level of the three models indicated that they are significant, and the barriers significantly influence the use of blockchain smart contracts for the three procurement systems. The findings also predicted that the design and build (DB) procurement system is best suited for the easier adoption of blockchain smart contracts in the Nigerian architectural, engineering and construction (AEC) industry. This novel study has developed statistical impact models of blockchain barriers on the critical procurement systems in the Nigerian AEC industry, giving a road map for blockchain smart contracts usage.
This editorial introduces the Special Issue on Emerging Digital Platforms and Non-Fungible Tokens (NFTs). The collected articles explore how disintermediation through blockchain technology is redefining value creation, ownership models, financial stability, and decentralized governance. Key insights are provided on the shift from centralized to community-driven value systems, the emergence of innovative business models linking physical and digital assets, and the integration of traditional and digital financial tools for enhanced risk management. The issue also examines the complexities of decentralization, highlighting both the potential for democratizing finance and the challenges related to the concentration of influence within decentralized networks. Together, these studies provide a comprehensive understanding of how DeFi, in general, and NFTs, in particular, are reshaping economic interactions, fostering new forms of participation, and creating opportunities for more transparent and equitable economic systems.
Mohammad Yaser Mofatteh, Ujjwal Khadka, Omid Fatahi Valilai
Energy management can be designed from different perspectives including production, distribution, and consumption. Focusing on consumption perspective, manufacturing systems can be enhanced by enabling smart machines as agents which operate with their own knowledge representation models in a shopfloor. These agents can benefit from industry 4.0 enablers like IoT including sensors, controllers, and actuators. This paper focuses on how these agents can interoperate with each other and exchange knowledge to optimize energy consumption. Since different knowledge models may not be capable of interacting with other ones based on their different provider semantics. This paper explores the application of blockchain technology for secure, decentralized storage and sharing knowledge models in smart energy systems. The research introduces EnerChain as a blockchain-integrated and a decentralized application (DApp) system prototype that employs smart contracts for access management and conflict resolution. It also incorporates the InterPlanetary File System (IPFS) for efficient off-chain storage, addressing scalability concerns. The feasibility and practicality of this approach are demonstrated through the development of EnerChain. The findings highlight the significant potential of blockchain technology in facilitating efficient knowledge model management for smart shopfloors. Additionally, an operational scenario has been evaluated as a case study for the proposed conceptual model to illustrate how it can solve energy conflicts in a smart environment. An impact analysis at the end of this research shows that EnerChain can make annual 27.5 TWh reduction in residential energy consumption which yields to annual 7.8 million tonnes reduction in CO2 emissions and annual €8.25 billion financial benefits.
Muhamed Turkanović, Vid Keršič, Alen Horvat
et al.
Delegation of authority remains a critical yet insufficiently addressed capability in Self-Sovereign Identity (SSI) systems. Building on an existing delegation model that introduced the concept of a Verifiable Mandate (VM) for expressing authority and access rights, this paper extends the approach with a rigorous formalization of delegation semantics, enabling unambiguous reasoning over roles, grants, and constraints. The formal model is aligned with standards from the World Wide Web Consortium (W3C), and its constructs are embedded into an extended credential schema that preserves compatibility with the Verifiable Credentials (VC) data model while introducing delegation-specific attributes. A generalized VM schema is defined, supporting both generic and business-specific instantiations, and ensuring structural and semantic interoperability. Policy compliance is operationalized through a policy-based enforcement architecture, where rules are authored in the Rego language and evaluated at runtime by the Open Policy Agent (OPA). The architecture incorporates trusted registries for schema and policy distribution, allowing verifiers to define and enforce context-specific delegation rules in a modular and interoperable manner. Validation through realistic scenarios, such as postal service and academic use cases, demonstrates how formal semantics, schema validation, and language-based policy enforcement can be combined to enable secure, verifiable, and context-aware delegation in SSI ecosystems.
Ali Tavakoli Golpaygani, Fereshteh-Azadi Parand, Mohammad Amin Keshavarz
Nowadays, medical devices are widely used around the world. In every country, the safety of these devices is inextricably related to public health and security. Despite all these sensitivities and legal oversight, issues such as lack of transparency in the medical device supply chain, lack of real feedback from incidents and clinical reports related to these devices, and consequently the failure to update risk management systems and improve the quality of these devices, resulting in increased safety risks for users/patients. Therefore, the development and implementation of an effective post-market surveillance and management system for medical devices is essential.In this article, in addition to a review of some applications of blockchain technology in the health sector, the requirements and structural framework for an information and management system for post-market surveillance of medical devices based on this technology were also presented. This system was developed with the approach of tracking medical devices to enhance transparency, provide rapid information dissemination, and reduce the rate of adverse incidents, and clinical reports related to these devices throughout their supply chain. A blockchain-based post-market surveillance system for medical devices could provide all stakeholders with valuable and real recommendations and information in the form of smart contracts by ensuring complete informational transparency throughout the supply chain. Blockchain technology could be used by regulatory and oversight organizations to effectively monitor and manage the performance of medical devices, ensuring greater transparency, and thereby guaranteeing enhanced safety and quality for stakeholders.
Government data identification is a fundamental work in building a national integrated government big data system.This article reviewed the research progress of data identification technology, compared the similarities and differences in coding rules of different data identification technologies, and further reviewed the progress of government data identification and its applications.Based on the clear rights and responsibilities, high security requirements, and strong compatibility requirements of government data, the next generation government data identification system Gcode was proposed.Gcode consists of three parts: external code, internal code, and security code.Among them, the external code was compatible with the Code for Unified Social Information, the internal code established an association relationship of ＆quot;institution-departmentsystem-data＆quot;, and the security code achieved anti-counterfeiting verification by introducing blockchain technology.Gcode has clear rights and responsibilities, strong compatibility and high security, and can support cross-level, cross-region, crosssystem, cross-department, and cross-business sharing of government data, effectively promoting the implementation of＆quot;one data, one source＆quot; of government data.
Chong-Chuo Chang, Wing-Keung Wong, Shih-Tse Lo
et al.
Sovereign credit ratings, extensively studied for their influence on macroeconomics and country risk, have been less explored in the context of their impact on individual firms. This research delves into the effects of sovereign credit rating changes on firm risk. Our findings suggest that an upgrade in sovereign credit ratings decreases firm risk, while a downgrade amplifies it. Furthermore, the magnitude of a country's rating shift positively correlates with changes in firm risk. We also discern a contagion effect between trade-dependent countries: an elevated rating in one country diminishes the firm risk in its trading partner, and vice versa. When categorizing our data into developed and developing markets, we observe that firm risk in developed markets reacts more acutely to rating upgrades. Conversely, rating downgrades, whether domestic or in trade-associated countries, intensify firm risk in developing markets. A robustness check, which evaluates sovereign credit rating fluctuations outside of financial crises, corroborates our core findings.
Bruno Miguel Batista Pereira, José Manuel Torres, Pedro Miguel Sobral
et al.
Since its appearance in 2008, blockchain technology has found multiple uses in fields such as banking, supply chain management, and healthcare. One of the most intriguing uses of blockchain is in voting systems, where the technology can overcome the security and transparency concerns that plague traditional voting systems. This paper provides a thorough examination of the implementation of a blockchain-based voting system. The proposed system employs cryptographic methods to protect voters’ privacy and anonymity while ensuring the verifiability and integrity of election results. Digital signatures, homomorphic encryption (He), zero-knowledge proofs (ZKPs), and the Byzantine fault-tolerant consensus method underpin the system. A review of the literature on the use of blockchain technology for voting systems supports the analysis and the technical and logistical constraints connected with implementing the suggested system. The study suggests solutions to problems such as managing voter identification and authentication, ensuring accessibility for all voters, and dealing with network latency and scalability. The suggested blockchain-based voting system can provide a safe and transparent platform for casting and counting votes, ensuring election results’ privacy, anonymity, and verifiability. The implementation of blockchain technology can overcome traditional voting systems’ security and transparency shortcomings while also delivering a high level of integrity and traceability.
Rajanikanth Aluvalu, Senthil Kumaran V. N., Manikandan Thirumalaisamy
et al.
In the medical era, wearables often manage and find the specific data points to check important data like resting heart rate, ECG voltage, SPO2, sleep patterns like length, interruptions, and intensity, and physical activity like kind, duration, and levels. These digital biomarkers are created mainly through passive data collection from various sensors. The critical issues with this method are time and sensitivity. We reviewed the newest wireless communication trends employed in hospitals using wearable technology and privacy and Block chain to solve this problem. Based on sensors, this wireless technology controls the data gathered from numerous locations. In this study, the wearable sensor contains data from the various departments of the system. The gradient boosting method and the hybrid microwave transmission method have been proposed to find the location and convince people. The patient health decision has been submitted to hybrid microwave transmission using gradient boosting. This will help to trace the mobile phones using the calls from the threatening person, and the data is gathered from the database while tracing. From this concern, the data analysis process is based on decision-making. They adapted the data encountered by the detailed data in the statistical modeling of the system to produce exploratory data analysis for satisfying the data from the database. Complete data is classified with a 97% outcome by removing unwanted data and making it a 98% successful data classification.
Human resource data sharing is very important for the cooperation of human resource institutions. Since the human resource data-sharing service needs to take into account the needs of individuals and talent service institutions, it faces issues such as the security of information sharing and the traceability of information. Therefore, this paper constructs a human resource data-sharing service system based on blockchain technology. Its trust mechanism is based on the Fabric alliance chain, and the system makes full use of its advantages of decentralization and consensus. Our research mainly includes data sharing architecture design, consensus mechanism analysis, smart contract design, data sharing process, and blockchain construction. The contribution of this paper is mainly in two aspects. On the one hand, it explores the trust mechanism of human resource data sharing and gives the scheme of the Fabric alliance chain. On the other hand, the overall architecture and smart contract design are given on the construction of the blockchain, which provides a reference for future research on human resource data sharing.
The emergence of the Bitcoin cryptocurrency marked a new era of illegal transactions. Cryptocurrency provides some level of anonymity allowing its users to create an unlimited number of wallets with alias addresses, which makes it challenging to identify the actual user. This is used by criminals for the purpose of making illegal transactions. At the same time, Bitcoin stores and provides information about all committed transactions, which opens up opportunities for identifying suspicious behavior patterns in this network using data mining. The problem of detecting suspicious activity in the Bitcoin network can be solved with sufficiently high accuracy using machine learning methods. The paper provides a comparative study of various machine learning methods to solve the mentioned problem: logistic regression, decision tree, random forest, gradient boosting. Selecting hyper parameters, rebalancing the dataset, and active learning are particularly important. The most important hyperparameters of the algorithms are described. Metrics show that the gradient boosting looks the most promising. In total 38 features of bitcoin addresses were identified. The top features are presented in the paper.
Blockchains such as Bitcoin and Ethereum execute payment transactions securely, but their performance is limited by the need for global consensus. Payment networks overcome this limitation through off-chain transactions. Instead of writing to the blockchain for each transaction, they only settle the final payment balances with the underlying blockchain. When executing off-chain transactions in current payment networks, parties must access the blockchain within bounded time to detect misbehaving parties that deviate from the protocol. This opens a window for attacks in which a malicious party can steal funds by deliberately delaying other parties' blockchain access and prevents parties from using payment networks when disconnected from the blockchain. We present Teechain, the first layer-two payment network that executes off-chain transactions asynchronously with respect to the underlying blockchain. To prevent parties from misbehaving, Teechain uses treasuries, protected by hardware trusted execution environments (TEEs), to establish off-chain payment channels between parties. Treasuries maintain collateral funds and can exchange transactions efficiently and securely, without interacting with the underlying blockchain. To mitigate against treasury failures and to avoid having to trust all TEEs, Teechain replicates the state of treasuries using committee chains, a new variant of chain replication with threshold secret sharing. Teechain achieves at least a 33X higher transaction throughput than the state-of-the-art Lightning payment network. A 30-machine Teechain deployment can handle over 1 million Bitcoin transactions per second.