Parisa Bouzari, Maria Fekete-Farkas, Zsigmond Gábor Szalay
This research investigates the efficacy of transformer architectures in classifying sustainability claims made by cryptocurrency projects, addressing a critical gap in automated environmental impact assessment of digital assets. Employing design science research (DSR) methodology, we develop and empirically evaluate a novel framework comparing five state-of-the-art transformer models across multiple performance dimensions. Through rigorous analysis of 300 synthetic cryptocurrency sustainability news articles, we demonstrate that RoBERTa-large-MNLI achieves optimal performance (F1: 1.00) with exceptional prediction stability (0.98)—meaning highly consistent predictions across varied inputs—and minimal entropy (0.05)—indicating strong confidence in classification decisions—albeit at higher computational costs. Our findings challenge conventional assumptions about the inverse relationship between model complexity and prediction reliability in specialized financial domains. The results advance theoretical understanding of transfer learning in sustainable finance while establishing quantitative benchmarks for automated environmental claim verification. This research contributes to both academic literature and regulatory frameworks by providing empirically validated methodologies for distinguishing between substantive and symbolic environmental initiatives in cryptocurrency markets. The findings provide valuable guidelines for cryptocurrency projects, financial institutions, and regulatory bodies seeking to implement automated sustainability assessment systems, while establishing a foundation for future research in the intersection of artificial intelligence and sustainable finance.
The global Blockchain Market, valued at USD 24.20 billion in 2024, is projected to reach USD 301.02 billion by 2030, expanding at an impressive CAGR of 60.2% from 2025 to 2030. The increasing digital payment transactions, rising demand for security, and rapid adoption of cryptocurrencies are fueling market growth. However, challenges such as regulatory uncertainty and high implementation costs remain significant barriers. Despite these limitations, the integration of decentralized finance (DeFi), AI, and advanced technological frameworks presents substantial opportunities for innovation. Leading industry players, including IBM, Ethereum, Hyperledger, and Oracle, are actively engaging in partnerships and technological advancements to strengthen their market presence. As blockchain technology continues to evolve, its applications are expanding across sectors including BFSI, healthcare, government, logistics, and retail. This manuscript provides an in-depth analysis of drivers, restraints, opportunities, segmentation, regional insights, and competitive landscape shaping the future of the global blockchain economy.
Pedro F. F. Abreu, Maria R. F. M. Ferreira, Luis H. O. Mendes, Geraldo A. Sarmento Neto · 8 authors
The proliferation of Internet of Things (IoT) devices necessitates secure, scalable, and cost-effective access control mechanisms. While blockchain and Non-Fungible Tokens (NFTs) offer a decentralized paradigm for managing permissions, they remain vulnerable to off-chain resource exhaustion attacks and present practical implementation challenges for low-cost devices. This paper proposes a novel hybrid architecture that enhances NFT-based access control with an off-chain gateway acting as both a Smart Reputation System (SRS) and a delegated signer. This hybrid model combines fast, off-chain pre-validation with authoritative on-chain verification. The SRS serves as a security firewall, mitigating high-frequency invalid requests by dynamically managing the reputation of each device and imposing temporary bans on malicious actors. By delegating cryptographic signing to the gateway, low-cost IoT devices are absolved of managing private keys, significantly reducing their complexity and cost. An experimental evaluation of the implemented system was conducted to assess its resilience against Denial-of-Service attacks. The findings indicate that the system successfully neutralizes threats in under 3 seconds. During this process, a stable end-to-end latency of approximately 626 ms is maintained for legitimate users, with the gateway’s reputation logic introducing a negligible performance overhead of less than 1%. This hybrid approach proves to be a practical and effective solution for deploying secure and resilient access control in real-world IoT environments.
The integration of smart contracts within blockchain technology represents atransformative approach to intellectual property rights (IPR) management, fundamentally altering traditional copyright enforcement mechanisms. This article demonstrates how distributed computer networks combined with automated regulatory devices provide superior alternatives to conventional IPR handling methods. Smart contracts reduce the need for arbitration through automated execution of predetermined terms and coding protocols. The implementation of blockchain-based smart contract systems enhances proprietary rights management, which can be particularly relevant for the BRICS nations currently facing evolving digital governance challenges. Research indicates that automated proprietary system networks are progressively superseding traditional IPR management approaches. The development of automated governance systems, coupled with decentralized IPR frameworks, presents both opportunities and regulatory challenges for the BRICS countries. Embedded payment mechanisms within smart contracts ensure automatic royalty distribution when copyrighted content is accessed, eliminating manual processing burdens and associated costs for creators. The implementation of smart contracts also enhances agreement integrity and reduces plagiarism risks through the use of immutable blockchain records. This study examines how organizations can establish enhanced trustworthiness and optimize digital business processes through blockchain-based copyright management. Advanced analytical tools accelerate the understanding of both the benefits and limitations within current copyright frameworks. Users are able to seamlessly access blockchain systems, creating multiple account types as required. Every blockchain entry provides transparent records of content usage and account activities. The digital system prevents misrepresentation by maintaining visible platform activities that are accessible to all stakeholders, ensuring comprehensive transparency of development and execution history for all agreement participants.
Federated Learning (FL) has become a promising method for training machine learning models while protecting patient privacy. This systematic review examines the use of privacy-preserving techniques in FL within decentralized healthcare systems. It compares existing methods such as Differential Privacy (DP), Trusted Execution Environment (TEE), Zero Knowledge Proofs (ZKP), Homomorphic Encryption (HE), Watermarking, Blockchain, and Secure Multi-Party Computation (SMPC) based on regulatory compliance, scalability, computational cost, complexity, and mathematical foundations. The principle challenges in decentralized healthcare like heterogeneous data, privacy risks, security threats, and compliance issues have been discussed. The review also highlights the importance of adhering to global regulations like HIPAA, GDPR, and country-specific data protection laws. Furthermore, it discusses open challenges and suggests future research directions to overcome current limitations, including computational efficiency, adversarial attacks, and the creation of policy frameworks for standardization. Overall, this review provides a unique perspective on ethical, secure, and scalable privacy-preserving FL models for the next generation of healthcare applications. • Analyzes essential techniques: Differential Privacy, SMPC, HE, TEE, ZKP, and Blockchain. • Reviews key privacy techniques: DP, SMPC, HE, TEE, ZKP, and Blockchain. • Compares methods based on cost, scalability, and resilience in FL. • Identifies issues such as non-IID data, high communication, and compliance. • Suggests hybrid and hardware-aided frameworks for secure FL. • presents future needs in terms of explainability, interoperability, and quantum security.
A system design built on blockchain technology presents a fundamental challenge: the inherent transparency of the blockchain conflicts with the growing need for user privacy. This dissertation explores how Zero-Knowledge Proofs (ZKPs) can be strategically combined with blockchain to strike a balance between these competing demands. The dissertation analyzes the challenges of privacy and transparency and provides an overview of solutions across the privacy-transparency solution space, drawing from the author’s original research and the broader academic landscape. On the privacy-centric side, it proposes a privacy-preserving design that utilizes off-chain ZKPs. In contrast, on the transparency-centric side, it presents a transparency-enhancing design that leverages on-chain data to build trust. In the middle, it discusses a taxonomy of hybrid applications whose unique combination of on-chain privacy (via ZKPs) and blockchain transparency reveals both a disruptive potential and significant regulatory challenges.
The study proposes a blockchain-based framework to overcome the challenges of data silos, privacy risks, and interoperability limitations in pharmacovigilance systems, focusing on the refinement of adverse drug reaction (ADR) data collection, storage, and analysis. Incorporating blockchain's transparency, immutability, and security, the framework comprises four core components: a data collection layer for multi-source ADR reporting, a standardization layer for data integration and validation, a blockchain network layer for tamper-proof storage and secure sharing, and a data analysis layer for real-time risk detection and visualization. The framework's efficacy in drug safety monitoring and regulatory efficiency is exemplified by the MediLedger and Merck's SAP Pharma Blockchain Proof of Concept case studies. The proposed resolution shows remarkable potential for advancing global pharmacovigilance practices.
Modern cyber threats, known for their complexity and constant change, surpass traditional intrusion detection systems (IDS). This paper explores a new security approach that combines Artificial Intelligence (AI) with decentralized architectures to develop IDS that are robust, scalable, and protect user privacy. It examines the core roles of Federated Learning (FL) and Blockchain, highlighting three main research challenges: The vulnerability of AI models to adversarial attacks, privacy and data integrity concerns in collaborative learning, and performance limitations in distributed systems. To address these issues, we suggest solutions such as adversarial training, differential privacy, and lightweight consensus mechanisms. Our analysis of case studies shows that hybrid FL-Blockchain systems outperform traditional methods in practical application environments.
Oleg Kravets, Ali Husein, Diana Getmanskaia, Mustafa Al-Imari · 7 authors
The article discussed an automata model of the functioning process of a system with distributed ledger technology based on a blockchain. The goal is to develop a special mathematical system with a distributed ledger based on a blockchain, taking into account the specifics of implementing algorithms for mutual information coordination, the possibility of combining individual nodes into groups, and implementing alternative strategies based on the development of appropriate models and algorithms that ensure increased stability in their operation. Automata theory was used, theory and methods of mutual coordination were applied, and the mechanism of the functioning process of a system with a distributed register was implemented. An automata model has been obtained and investigated. Thus, an automata model of the distributed ledger technology blockchain system node functioning process has been developed, which differs from the node representation by a finite state machine with a variable structure and a linear tactic with the possibility of implementing non-standard functions: the formation of a branch of processed data and a temporary blockage attack, and provides for obtaining the dependence of the sequence of changing the node's behaviour strategy options on the conditions of the environment it interacts with.
A system implementaion paper on a decentralized application for transparent charity transactions built on hardhat , next js ,and django that combines the web2 and web3 dynamics.
We examine the qualifying attributes of decentralized finance (DeFi) as a financial asset class. To achieve this objective, we perform analysis on the relationship (using both level and percentage-change data) between DeFi valuation and selected influencing variables, namely total value locked (TVL), Bitcoin (BTC) value, and market variables. A suite of long-panel data econometric methods is employed on a multi-frequency (daily, weekly, and monthly) panel dataset comprising 16 major DeFi protocols from January 2022 to December 2023. Our empirical design aims to be a comprehensive assessment and triangulation. There are several key findings. First, while there is evidence of cointegration suggesting a possible long-run relationship, this relationship is found to be inconsistent across different variables and time frequencies. However, the impulse response analysis suggests that shocks from the influencing variables do not have a permanent impact. Second, Bitcoin value is found to be the most important influencing factor (positive and highly significant), reflecting strong cryptocurrency market sentiment and aligning with previous research on spillover effects from major cryptocurrencies (Șoiman et al., 2022; Yousaf et al., 2022).
Social sustainability in urban and architectural design depends on inclusive, participatory processes that empower communities to actively engage in shaping their environments. This study investigates how emerging digital platforms, specifically Augmented Reality (AR) and decentralized platforms built on blockchain technology (Web3), can function as instruments for broadening public participation and enhancing perceptual access to urban art proposals. An original algorithm generated nine digital abstract sculptures, each with descriptive attributes forming the basis for qualitative analysis across different visualization modes: traditional renderings, Augmented Reality environments, and NFT-based Web3 representations. Through participant voting, each digital sculpture accumulated a measurable level of preference that served to identify which sculpture was perceived as most successful within each visualization context. Comparative analysis revealed how distinct digital interactions shape perception, engagement, and inclusivity of feedback processes. Regression models further predicted voting outcomes, showing that different sculptural attributes played a dominant role depending on the type of visualization. Findings indicate that platform-specific technological affordances substantially shape participatory outcomes. Consequently, the study argues that careful analysis and selection of the digital platform must precede any participatory process, as platform-specific affordances fundamentally condition the inclusivity, accessibility, and overall effectiveness of public engagement in socially sustainable design.
This paper introduces Lean 5.0, a human-centric evolution of Lean-Digital integration that connects predictive analytics, AI collaboration, and continuous learning within Industry 5.0 and Construction 5.0 contexts. A systematic literature review (2019-2024) and a 12-week empirical validation study demonstrate measurable performance gains, including a 13% increase in Plan Percent Complete (PPC), 22% reduction in rework, and 42% improvement in forecast accuracy. The study adopts a mixed-method Design Science Research (DSR) approach aligned with PRISMA 2020 guidelines. The paper also examines integration with digital twin and blockchain technologies to improve traceability, auditability, and lifecycle transparency. Despite limitations related to sample size, single-case design, and study duration, the findings show that Lean 5.0 provides a transformative paradigm connecting human cognition with predictive control in construction management.
Opening up data produced by the Internet of Things (IoT) and mobile devices for public utilization can maximize their economic value. Challenges remain in the trustworthiness of the data sources and the security of the trading process, particularly when there is no trust between the data providers and consumers. In this paper, we propose DEXO, a decentralized data exchange mechanism that facilitates secure and fair data exchange between data consumers and distributed IoT/mobile data providers at scale, allowing the consumer to verify the data generation process and the providers to be compensated for providing authentic data, with correctness guarantees from the exchange platform. To realize this, DEXO extends the decentralized oracle network model that has been successful in the blockchain applications domain to incorporate novel hardware-cryptographic co-design that harmonizes trusted execution environment, secret sharing, and smart contract-assisted fair exchange. For the first time, DEXO ensures end-to-end data confidentiality, source verifiability, and fairness of the exchange process with strong resilience against participant collusion. We implemented a prototype of the DEXO system to demonstrate feasibility. The evaluation shows a moderate deployment cost and significantly improved blockchain operation efficiency compared to a popular data exchange mechanism.
Andreea Elena Drăgnoiu, Andrei Ciobanu, Ruxandra F. Olimid
Self-Sovereign Identity (SSI) grants holders full ownership and control of their digital identities, being the ultimate digital identity model. Operating in a decentralized manner, SSI enables the verification of claims, including privacy-preserving mechanisms. Blockchain, which can be used to implement a Verifiable Data Registry (VDR), is often considered one of the pillars of SSI, along with Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Unfortunately, blockchains are mostly siloed, affecting the interoperability and universality of SSI. We investigate the effect of blockchain isolation on blockchain-based SSI. We first define possible scenarios for cross-chain SSI and exemplify with real-life use cases. We then define specific requirements for cross-chain SSI and identify challenges, also in relation to the identified scenarios. We explore various solutions to achieve blockchain interoperability, with a focus on SSI. In particular, we identify the advantages and disadvantages of distinct cross-chain models for cross-chain SSI. Finally, we address the usability of cross-chain SSI and discuss security and privacy aspects, opening the way for future research.
Entrepreneurs typically seek financing in decentralized markets, where they approach investors sequentially. We develop a model of sequential capital markets with privately informed investors. The sequential market creates a dynamic adverse selection externality that leads to overinvestment and excessive rents to intermediaries, even as the number of competing investors becomes arbitrary large. The resulting rents lead to excessive entry of investors and insufficient entry of entrepreneurs. Moving to a centralized market structure or reducing transparency restores competitiveness but may harm efficiency. The model also explains how even a small skill advantage for an investor can lead to preferential deal flow and outsized returns.
This study examines the adoption of Digital Ledger Technology (DLT) and its impact on the accuracy of financial reporting and the efficiency of auditing processes within Jordanian organizations. Using a quantitative research design, the study assesses how DLT enhances financial data integrity and supports real-time auditing capabilities. Data were collected from 210 accounting and auditing professionals representing five major Jordanian institutions: the Central Bank of Jordan, Jordan Customs Department, Arab Bank, Deloitte Jordan, and Ernst & Young Jordan. A structured questionnaire served as the primary data collection instrument, employing a five-point Likert scale to measure perceptions across key constructs related to DLT adoption. To improve response rates, the questionnaire was distributed through both physical and digital channels. Descriptive statistics were used to analyze demographic data, while multiple regression and independent samples t-tests were applied to test the study’s hypotheses. The results revealed a statistically significant and positive relationship between DLT adoption and both the accuracy and reliability of financial reporting, as well as between DLT utilization and enhanced auditing speed and efficiency. Regression analysis indicated that DLT adoption accounted for 52% of the variance in financial reporting accuracy, while t-test results confirmed significant differences between DLT-based and traditional auditing methods. The study complied with ethical standards, ensuring confidentiality and voluntary participation. Overall, the findings demonstrate that DLT plays a transformative role in improving accounting and auditing practices within a developing economy context.
One-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication architecture that integrates a permissioned Hyperledger Fabric blockchain for tamper-evident identity management, an AI-driven risk engine for behavioral and SIM-swap anomaly detection, Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification, and geolocation-bound OTP validation for contextual assurance. Hyperledger Fabric is selected for its permissioned governance, configurable endorsement policies, and deterministic chaincode execution, which together support regulatory compliance and high throughput without the overhead of cryptocurrency. The system is implemented as a set of modular microservices that combine encrypted off-chain storage with on-chain hash references and smart-contract–enforced policies for geofencing and privacy protection. Experimental results show sub-0.5 s total verification latency (including ZKP overhead), approximately 850 transactions per second throughput under an OR-endorsement policy, and an F1-score of 0.88 for SIM-swap detection. Collectively, these findings demonstrate a scalable, privacy-centric, and interoperable solution that strengthens OTP-based authentication while preserving user confidentiality, operational transparency, and regulatory compliance across mobile network operators.
T J E N N I N G, Kalokhe Omkar Nanabhau, Takale Ram Arjun, Borge Akash Sandip
The proliferation of digital documents and academic credentials in today's interconnected world has created both opportunities and vulnerabilities. Traditional certificate issuance and storage mechanisms are highly susceptible to forgery, duplication, and unauthorized manipulation, undermining the trustworthiness of academic and professional qualifications. To address these challenges, this research proposes a blockchain-based certificate generation and verification system that ensures transparency, immutability, and trust across stakeholders. Leveraging distributed ledger technology, the system securely records certificate metadata and unique identifiers, enabling real-time, tamper-proof validation without reliance on intermediaries. The architecture integrates modern web technologies such as Next.js for frontend and backend services, MongoDB for scalable storage, JWT for authentication, and cryptographic techniques including bcrypt for enhanced security. Additionally, smart contracts deployed on Ethereum/Ganache enable decentralized storage and validation, while certificate data is simultaneously linked with non-fungible tokens (NFTs) to provide verifiable ownership and authenticity. This integration not only eliminates certificate fraud but also facilitates seamless verification across institutions, employers, and regulatory authorities. By combining blockchain's decentralized security with user-friendly web applications, the proposed approach aims to create a globally interoperable, cost-effective, and future-ready framework for academic and professional certification systems.
This research aims to analyze the readiness of Indonesia’s legal framework to accommodate Non-Fungible Tokens (NFTs) as fiduciary collateral objects and to identify the potential obstacles in their execution process. NFTs are digital assets based on blockchain technology that possess unique characteristics and economic value, theoretically fulfilling the criteria of intangible objects under Law Number 42 of 1999 concerning Fiduciary Collateral. However, the current legal instruments in Indonesia have not yet explicitly recognized NFTs as eligible fiduciary objects. This study employs a normative juridical method with both statutory and case approaches, supported by secondary data derived from an interview with the Direktorat Jenderal Administrasi Hukum Umum (Ditjen AHU) to enrich the legal analysis. The findings indicate that Indonesia’s regulatory readiness regarding NFTs as fiduciary collateral remains conceptual rather than operational. The absence of a registration mechanism and valuation system for digital assets creates significant legal uncertainty for both creditors and debtors. Furthermore, the execution of NFTs presents additional challenges, including the dependence on private keys, the incompatibility of automatic smart contract transfers with the due process of law principle, and the speculative volatility of NFT market values. Therefore, the study suggests the need for new legal norms and an authorized digital asset valuation institution to ensure that NFTs can be effectively and lawfully integrated into Indonesia’s fiduciary security system. Non-Fungible Token, Fiduciary Collateral, Collateral Execution, Intangible Assets
This study presents a systematic literature review (SLR) conducted under the PRISMA 2020framework to investigate the convergence of two transformative paradigms: Generative ArtificialIntelligence (GenAI) and Web3. The findings indicate that, while each technology independentlydrives digital transformation, their integration remains underexplored. GenAI advancesinnovation through algorithmic creativity, personalization, and automated content generation,whereas Web3, enabled by blockchain, smart contracts, non-fungible tokens (NFTs), anddecentralized autonomous organizations (DAOs), introduces decentralized mechanisms of trust,transparency, and digital ownership. Current research addressing the intersection of thesedomains is fragmented and predominantly conceptual, leaving critical gaps in trust mechanisms,governance structures, operational models, and legal frameworks.To address these gaps, this study proposes the conceptual AIChain Framework: a unifiedplatform that integrates GenAI-powered content generation, automated tokenization, trustengines, and decentralized marketplaces. This architecture demonstrates cross-sectoral potentialin creative industries, FinTech, and education by linking algorithmic creativity withdecentralized ownership. The contributions are threefold: (1) at the theoretical level, the studysynthesizes the Resource-Based View (RBV), the Dynamic Capabilities View (DCV), the digitaltrust framework, and the information interaction model to establish a foundation for analyzingGenAI–Web3 convergence; (2) at the practical level, it introduces an operational architecture fornext-generation platform development; and (3) at the policy and governance level, it highlightsthe need for transparent, auditable, and participatory models to prevent technological oligopolies.By bridging theoretical insights with practical implications, this research provides a roadmap forfuture scholarship and industry practice, including pilot implementations of the AIChainframework, the design of hybrid governance models, and the assessment of ethical andenvironmental implications surrounding GenAI–Web3 convergence.
Forecasting cryptocurrency prices is hindered by extreme volatility and a methodological dilemma between information-scarce univariate models and noise-prone full-multivariate models. This paper investigates a partial-multivariate approach to balance this trade-off, hypothesizing that a strategic subset of features offers superior predictive power. We apply the Partial-Multivariate Transformer (PMformer) to forecast daily returns for BTCUSDT and ETHUSDT, benchmarking it against eleven classical and deep learning models. Our empirical results yield two primary contributions. First, we demonstrate that the partial-multivariate strategy achieves significant statistical accuracy, effectively balancing informative signals with noise. Second, we experiment and discuss an observable disconnect between this statistical performance and practical trading utility; lower prediction error did not consistently translate to higher financial returns in simulations. This finding challenges the reliance on traditional error metrics and highlights the need to develop evaluation criteria more aligned with real-world financial objectives.
This paper introduces a hybrid framework for portfolio optimization that fuses Long Short-Term Memory (LSTM) forecasting with a Proximal Policy Optimization (PPO) reinforcement learning strategy. The proposed system leverages the predictive power of deep recurrent networks to capture temporal dependencies, while the PPO agent adaptively refines portfolio allocations in continuous action spaces, allowing the system to anticipate trends while adjusting dynamically to market shifts. Using multi-asset datasets covering U.S. and Indonesian equities, U.S. Treasuries, and major cryptocurrencies from January 2018 to December 2024, the model is evaluated against several baselines, including equal-weight, index-style, and single-model variants (LSTM-only and PPO-only). The framework's performance is benchmarked against equal-weighted, index-based, and single-model approaches (LSTM-only and PPO-only) using annualized return, volatility, Sharpe ratio, and maximum drawdown metrics, each adjusted for transaction costs. The results indicate that the hybrid architecture delivers higher returns and stronger resilience under non-stationary market regimes, suggesting its promise as a robust, AI-driven framework for dynamic portfolio optimization.
The emergence of cryptocurrency has radically threatened traditional fiscal institutions, presenting Indian policymakers with an unrivaled challenge in tax administration and revenue generation. This review takes into account the multifaceted complexities of taxation of cryptocurrency in India, addressing regulatory uncertainties, enforcement issues, and the relevance for tax revenue composition. Drawing on policy trends and scholarly literature at the moment, the present paper investigates India's evolving stance on digital assets—from absolute skepticism to cautious engagement—and how this reflects deeper anxieties around innovation and control. The argument draws on strategic management, technology disruption within financial markets, and organizational change management to situate India's policy measures in context. Even as the 2022 budget introduced specific tax provisions for virtual digital assets, significant concerns continue to exist about mechanisms for compliance, global coordination, and long-term viability of prevailing strategies. India's cryptocurrency taxation regime is discovered to be in the early stages, with adaptive strategies required to reconcile revenue and technological innovation.