The emergence of blockchain technologies is changing how we manage data through decentralized, secure systems. In the realm of consensus mechanisms, such as PoW, PoS, and PBFT, several limitations make these technologies inadequate for handling the challenges of IoT-enabled environments and Mutual Distributed Ledgers (MDLs), which require constant and reliable access to their data. These consensus models are reactive, resulting in increased response times (latencies) when a failure or disruption occurs, decreased throughput, and extended recovery periods. The lack of adaptive intelligence to recognize and recover from failures in real-time exacerbates these network failures. This research introduces the Predictive Consensus Algorithm to Blockchain Networks with Failure Detection and Recovery in Real-Time (PCB-FDAR). PCB-FDAR provides a new mechanism by integrating machine learning-based predictive analytics with real-time network monitoring to anticipate future failures and automatically reconfigure the network without human intervention. The framework also enables fault-tolerance across interconnected blockchain environments. PCB-FDAR has been shown through experimentation to outperform traditional consensus mechanisms. When comparing chipsets with an average of 40 blocks, the PCB-FDAR framework achieves an average latency of 1,600 ms, which represents a 42.86% reduction from PoW (2,800 ms) and a 36.00% reduction over PBFT (2,500 ms). In addition, when performing scalability testing, PCBFDAR delivers as high as 1,800 transactions per second (TPS), representing a 450 Ă improvement over PoW (4 TPS) and a 32.7 Ă improvement over PoS (55 TPS). Lastly, the PCBFDAR automatic recovery mechanism reduces failure recovery time from 180 to 30 s, resulting in an 83.33% decrease and providing 99% operational availability. Thus, the results of this study demonstrate that PCB-FDAR provides a scalable, reliable, and fault-tolerant consensus framework for real-time distributed applications.
The growing adoption of digital medical health care systems makes it necessary to build efficient, secure, and interoperable medical information exchange services. Nevertheless, existing traditional healthcare systems are centralized, inefficient in communication, vulnerable in terms of the integrity of data, and lack transparency. In this study, a novel blockchain-based secure framework is proposed with the integration of Ethereum smart contracts, CBOR compression, AES-256 GCM encryption, and SHA-256 validation. A multispecialty hospital dataset including patientsâ information, laboratory information, prescriptions, and billing details is used in testing. A study obtained a compression rate of 7.22, validation speed of 0.0039 ms, encryption in 0.36 ms, average API latency of 98.47 ms, and throughput capacity of 52.9 TPS with a blockchain-based proposed system. Security analysis proved that this system provides security in terms of encryption, tamper resistance, access control, and immutability. The study also contributes a new model of communication within the health sector, which is both lightweight and secure, and increases blockchain performance and security.
Fruit and vegetable supply chains generate heterogeneous data across production, storage, logistics, and sales, creating challenges for trusted data sharing, privacy protection, and real-time traceability across distributed supply-chain information systems. Conventional single-chain blockchains suffer from limited scalability, data redundancy, and low retrieval efficiency, making them inadequate for high-frequency full-process information management. This study proposes a multi-chain blockchain framework for trusted full-process information management of fruit and vegetable supply chains. The framework integrates traceability, enterprise, notary, and regulatory chains to support hierarchical data management and privacy isolation. A reputation-based notary node election mechanism and a threshold-signature scheme based on Shamir secret sharing are designed to enhance cross-chain security and distributed regulatory consensus. To improve retrieval efficiency, a Cuckoo-Augmented Merkle Tree (CMerkle) and a skip-list-based block index are developed. Simulation results show that all malicious nodes were restricted by the 19th round, signature aggregation required 70.16 ms in a 500-node setting, and CMerkle achieved retrieval speedups of 14.7 and 153 times at data scales of 500 and 10,000 records, respectively. The framework supports trusted data governance, real-time traceability, privacy-preserving sharing, and regulatory decision support in blockchain-enabled supply-chain information systems.
This study investigates whether cryptocurrency adoption has affected Argentinaâs bilateral trade flows within a gravity-model framework. While blockchain-based technologies are often expected to reduce transaction costs and facilitate international trade, quantitative evidence on their actual impact remains limited. Using panel data on Argentinaâs trade with its main partners, the analysis combines standard gravity variables with country-level measures of cryptocurrency activity and estimates fixed effects, random effects, and high-dimensional fixed effects models.The results confirm the continued relevance of traditional trade determinants. Distance shows a robust negative effect on bilateral trade, with an elasticity ranging from â0.54 to â1.65 (p<0.05) across specifications. Country contiguity is associated with a 3.5-fold increase in bilateral trade (coefficient: +1.25, p<0.01). The effect of cryptocurrency adoption, by contrast, varies across specifications: in the random effects model, it is negatively associated with formal trade (â0.049, p<0.01), while in the correctly specified PPML model with origin-destination-year fixed effects, the contemporaneous effect is statistically insignificant. However, when cryptocurrency adoption is lagged one period, it shows a positive and highly significant association with trade (+0.061, p<0.01), suggesting that the trade-facilitating effect of crypto infrastructure may operate with a delay. We also find marginal evidence (pâ0.10) that cryptocurrency adoption attenuates the trade-reducing effect of distance. This counterintuitive result may indicate that cryptocurrency adoption substitutes for formal trade channels or reflects periods of economic instability, including the COVID-19 pandemic. However, this relationship is not robust to more demanding specifications that control for unobserved heterogeneity.Overall, the findings suggest that blockchain-based technologies have not yet translated into measurable trade-facilitating effects, partly due to limited institutional support and legal uncertainty. The paper highlights the gap between the potential benefits of blockchain for international trade and its actual adoption, emphasising the role of coordinated institutional frameworks in enabling technological diffusion.
The exploratory data analysis results provide important insights into the dataset characteristics that guide the design of the proposed AI-enabled blockchain framework. The class distribution graph shows a strong imbalance, with approximately 86.2% genuine samples and 13.8% forged samples, reflecting real-world conditions where fraudulent cases are relatively rare. This imbalance necessitates the use of robust machine learning strategies, such as class-weighted learning and advanced evaluation metrics beyond simple accuracy, to ensure reliable detection of forged instances. The file size distribution further indicates that most samples are lightweight, with an average size of 42.6 KB and a long-tailed distribution extending up to 295 KB, supporting the adoption of a hybrid on-chain/off-chain storage strategy to optimize blockchain storage costs and network performance. Dimensionality reduction and visualization results obtained using PCA and t-SNE highlight the complexity of the classification problem addressed in the proposed work. The PCA projection reveals partial overlap between genuine and forged samples, indicating that linear feature separation is insufficient for accurate classification. Similarly, the t-SNE visualization shows localized clustering of forged samples but noticeable overlap with genuine data, confirming the presence of non-linear relationships in the feature space. These observations justify the integration of deep learning models and ensemble classifiers within the AI layer to capture complex patterns and improve generalization. The image resolution distribution further demonstrates that most images fall within a consistent resolution range of approximately 300â700 pixels (width) and 200â550 pixels (height), ensuring stable model training while still requiring standardized preprocessing to handle resolution variability across training, validation, and test splits. Based on these data characteristics, the proposed AI-enabled blockchain framework is designed to deliver measurable improvements in performance, security, and efficiency. Experimental evaluation shows that the AI-driven valuation and classification modules achieve a fraud detection accuracy of 94.1%, with a precision of 91.6%, recall of 89.3%, and an F1-score of 90.4%, demonstrating reliable performance despite class imbalance. The blockchain layer achieves an average throughput of approximately 420 transactions per second with a confirmation latency of 2.6 seconds, while maintaining a low transaction cost of âč18ââč25 per transaction through Layer-2 scaling and off-chain storage optimization. Smart contracts exhibit a 99.1% execution success rate and high vulnerability detection coverage during security analysis, validating the robustness of automated transaction execution. The expected outcomes of the proposed system include reduced transaction settlement time, enhanced fraud resistance, improved valuation transparency, and greater market accessibility through tokenization and fractional ownership. By combining AI-driven intelligence with blockchain-based trust and automation, the framework is expected to significantly reduce manual intervention, operational costs, and regulatory non-compliance risks in real estate transactions. Overall, the results and projections confirm that the proposed approach is well-suited for real-world deployment, offering a scalable, secure, and intelligent solution for next-generation real estate asset management systems.
Blockchain data is immutable and publicly visible a sensitive signature is placed on-chain, anyone can attempt to verify it. This openness may lead to unintended information exposure. The standard ring signature cannot fully address all the privacy, selective-verification and time -controlled disclosure requirements that arise in modern secure systems. The proposed algorithm presents a Blockchain-based Ring Signature with Multi-Designated Verifier and Zero-Knowledge Proof (BRSMDV-ZKP) enables a signer anonymously authenticate a transaction with a group of public keys while ensuring that only designated verifiers can verify the signature, The scheme incorporates a challengeâresponse mechanism, randomized commitments, and encrypted verifiers specific data to ensure signer anonymity, trace resistance, and verifier exclusivity. A Zero-Knowledge Proof (ZKP) is employed to prove correct decryption of the signature without revealing the verifierâČs private key. The time-lock puzzle enforces a predefined delay, preventing early verification and enabling rewardâpenalty mechanisms for verifier compliance. This approach reduces the risk of key leakage, preserves privacy in decentralized systems and multi-party environments, and supports secure applications such as confidential e-voting, sealed-bid auctions, and legal document verification on blockchain platforms.
The ubiquity of lightweight resource-constrained Internet-of-Things (IoT) devices in 5G smart environments necessitates authentication protocols with the conflicting goals of being lightweight, highly secure, and having a decentralised credential management structure. Existing schemes use trusted third-party key distributors or heavyweight cryptographic primitives infeasible to IoT embedded hardware; they also fail to anchor device credentials on a permissioned blockchain ledger for tamper-evident credential revocation. In this work, we introduce BLAP-IoT: a Blockchain-Assisted Lightweight Authentication Protocol over live Hyperledger Fabric 2.5.9 that leverages elliptic-curve DiffieâHellman over P-256 curve, keyed MACs, and a three-message challenge-response protocol to provide injective mutual authentication with device key confirmation. Device credential commitments are stored on-chain to facilitate decentralised and efficient device revocation without revealing secrets on-chain. A formal security verification of the protocol in ProVerif 2.05 shows session-key secrecy, injective mutual authentication, and perfect forward secrecy in the presence of the Dolev-Yao attacker. The empirical evaluation of BLAP-IoT on measured P-256 primitives reports that the scheme achieves a total computation cost of 0.303 ms on constrained devices â up to 52% less than compared schemes, 1920-bit two-way communication overhead, and 0.218 mJ device energy consumption. The underlying blockchain layer sustains up to 277 transactions per second (TPS) in peak throughput, with end-to-end authentication latency less than 13 ms at 1000 concurrent devices.
This paper extends BlockSim by introducing energy-consumption and carbon-footprint metrics that are tightly integrated with its event-driven execution model, enabling sustainability-aware evaluation alongside conventional performance metrics. The proposed extension instruments core simulation events to estimate computational and communication energy at both node and network levels, then converts electricity demand to CO 2 emissions using an emission-factor formulation that can be configured to represent different grid carbon intensities. Using the extended simulator, controlled experiments are conducted on representative PoW and PoS consensus scenarios under varying miner populations and workloads. The framework reports aggregate energy and CO 2 , as well as normalized indicators per block and per transaction, supporting reproducible âwhat-ifâ analysis without external post-processing and enabling direct comparison of protocol configurations as networks scale. The framework additionally distinguishes economically driven Proof-of-Work (PoW) energy consumption, in which the expected mining reward and cryptocurrency price are explicit inputs, from validator-count-driven Proof-of-Stake (PoS) energy consumption; it varies the carbon emission factor across grid scenarios; and it is positioned as a scenario-based evaluation tool for preliminary what-if analysis rather than a precise real-world energy estimator.
With the rapid expansion of digital communication and data storage, cybersecurity has become a critical concern for organizations and individuals. Cryptographic methods play a vital role in ensuring data confidentiality, integrity, and authentication. This study explores the mathematical foundations of encryption, blockchain security, and post-quantum cryptography. Traditional encryption methods such as symmetric and asymmetric encryption rely on number theory and complex mathematical problems like integer factorization and discrete logarithms. Blockchain security is reinforced by cryptographic hashing and digital signatures, ensuring tamper-proof transactions. However, the advent of quantum computing poses a significant threat to existing cryptographic protocols, necessitating the development of post-quantum cryptographic methods. This research provides an in-depth analysis of current cryptographic techniques, evaluates their effectiveness, and discusses future advancements in quantum-resistant cryptography.
The rapid digitalization of financial services has increased the importance of blockchain, artificial intelligence (AI), and cybersecurity in strengthening operational efficiency, transaction security, fraud detection, and financial risk management. This study examines the relationship between blockchain technology adoption, AI-driven financial service adoption, cybersecurity capability, and financial risk reduction in the Indian financial-services context. Drawing on recent literature on blockchain-enabled financial services, AI-based risk management, cybersecurity, and digital banking, the study develops an empirical framework linking technology adoption with financial risk management effectiveness. Primary data were considered from 157 respondents comprising banking professionals, financial-service employees, FinTech professionals, IT specialists, and finance managers in India. Data were analyzed using descriptive statistics, Cronbachâs alpha, Pearson correlation, multiple regression, and ANOVA. The illustrative results indicate that blockchain adoption, AI adoption, and cybersecurity capability are positively associated with financial risk reduction. The regression model explains approximately 64.2% of the variance in financial risk reduction, with AI adoption emerging as the strongest predictor, followed by cybersecurity capability and blockchain adoption. The findings suggest that Indian financial institutions should adopt an integrated technology strategy rather than treating blockchain, AI, and cybersecurity as independent technological investments. Strong governance, employee capabilities, cybersecurity controls, regulatory alignment, and responsible AI practices are essential for converting technology adoption into sustainable financial-risk reduction.
Blockchain technology records transactions on a distributed ledger that is cryptographically chained, replicated across independent nodes, and validated by consensus rather than by any single institution. Because the technology verifies that recorded transactions occurred and have not been altered, some commentators have concluded that it will make external auditors redundant. This article rejects that conclusion but takes the underlying disruption seriously. It argues that blockchain automates a narrow and historically labor-intensive slice of the audit, namely the verification of the existence, occurrence, and mathematical accuracy of recorded transactions, while leaving untouched the components of assurance that depend on professional judgment: valuation, accounting estimates, classification, completeness of off-chain events, related party identification, and going concern assessment. At the same time, the technology creates new objects that require assurance, including consensus protocols, cryptographic key management, smart contract code, and the oracles that connect ledgers to the physical world. The article examines the consequences for auditing standards, particularly the treatment of blockchain records as audit evidence, and for the education, skills, and business model of the profession. The external auditorâs future role, it concludes, lies not in verifying transactions but in assuring the systems that now verify them, and in exercising the judgment that no ledger can encode.
This study examines the effect of blockchain adoption on market efficiency in selected African capital markets from 2014 to 2025. It is motivated by persistent inefficiencies in African stock exchanges, including weak liquidity, information asymmetry, delayed settlement, high transaction costs, and limited digital financial infrastructure. The study adopts a quantitative longitudinal panel design and develops a Blockchain Adoption Index covering blockchain infrastructure, settlement digitisation, fintech ecosystem indicators, and regulatory innovation. Market efficiency is measured using stock return predictability, bid-ask spread, price delay, turnover ratio, and information efficiency indicators, while institutional quality is introduced as a moderating variable. The study applies Dynamic Panel System Generalised Method of Moments estimation to address endogeneity, persistence effects, and unobserved heterogeneity. The findings show that blockchain adoption has a positive and statistically significant effect on market efficiency across African capital markets. Specifically, blockchain adoption improves liquidity, reduces informational frictions, narrows bid-ask spreads, and strengthens price discovery. The interaction result further shows that institutional quality enhances the positive effect of blockchain adoption on market efficiency. The study concludes that blockchain-enabled financial infrastructure can improve capital market performance in Africa when supported by strong governance, credible regulation, and effective digital infrastructure. It recommends increased investment in exchange digitisation, blockchain-based settlement systems, regulatory harmonisation, and institutional capacity development.
The study involved a total of 217 respondents. consisting of key stakeholders in higher education, including undergraduate and postgraduate students, academic staff, and administrative personnel. The respondents were selected using a purposive sampling technique to ensure they had relevant experience and understanding of academic data management systems. Among the participants, the majority were students, representing approximately 65%, followed by academic staff at 20%, and administrative personnel at 15%. In terms of gender distribution, 54% were female and 46% were male. Most respondents were aged between 18 and 30 years, reflecting a digitally active population familiar with emerging technologies. Additionally, a significant proportion of respondents reported prior exposure to digital academic systems, while a smaller percentage demonstrated awareness of blockchain technology applications in education. This distribution ensures that the collected data reflects diverse perspectives within the academic ecosystem and supports the reliability of the analysis conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS).
Blockchain technology offers a distributed, immutable ledger that can improve transparency, traceability and trust among multiple parties in logistics and supply-chain networks. This expanded review synthesises systematic literature from 2018â2025, presents key real-world case studies, quantifies reported benefits, catalogues persistent challenges, and examines the convergence of blockchain with IoT sensors, artificial intelligence and digital twins. Emphasis is placed on food traceability, maritime shipping, pharmaceuticals, sustainability reporting and the socio-technical conditions required for successful adoption. The review also draws on related recent work on supply-chain resilience, digital twins, AIâblockchain integration and emerging quantum approaches to logistics optimization. Illustrative figures and a market-growth curve accompany the analysis. Keywords: Blockchain; Logistics; Supply Chain Management; Traceability; Smart Contracts; Trade Lens; IBM Food Trust; Permissioned Ledger; Interoperability; Digital Twin; IoT Integration; Literature Review; Supply Chain Resilience; Quantum Logistics.
The rapid development of cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs) has transformed the global monetary landscape and accelerated the transition toward a cashless society. While critics argue that digital currencies threaten financial stability due to volatility, disintermediation, energy consumption, and regulatory concerns, this paper contends that the increasing competition among digital and fiat currencies can generate significant economic benefits. By examining the evolution of cryptocurrencies, the emergence of stablecoins, the global adoption of CBDCs, and the case of Zimbabwe's hyperinflation, this study argues that currency competition encourages governments to pursue more disciplined fiscal and monetary policies, strengthens policy credibility, and helps anchor inflation expectations. Greater monetary credibility also expands policymakers' ability to respond effectively to future economic downturns. Although digital currencies present important risks, many of these challenges can be mitigated through technological innovation, appropriate regulation, and institutional development. Overall, this paper concludes that a wellmanaged transition toward a cashless society can promote competition, innovation, and long-term economic resilience rather than undermine financial stability.
Although stablecoins occupy a segment of digital-asset markets in which price stability is central by design, their temporary departures from reference values may reveal important information about latent risk and market stress. In this paper, we examine whether bubble and crash signals extracted from traditional cryptocurrencies and stablecoins improve volatility, Value-at-Risk, and Expected Shortfall forecasting and, in connection with these forecasting gains, contribute to the assessment of cross-asset contagions. The analysis applies the Bubble CrashâGARCH models, in which extreme price phases are identified through the Phillips, Shi, and Yu real-time monitoring procedure and incorporated into the conditional mean of returns through event-based dummy variables. For stablecoins, extreme episodes are not inferred from price dynamics in isolation but from deviations between the observed price and the asset-specific reference value. The empirical investigation focuses on Bitcoin, Ethereum, Tetherâs USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubbleâcrash effects and bidirectional contagion channels between traditional cryptocurrencies and stablecoins, using Bitcoin and Tether as the leading representatives of the two market segments. The findings indicate that accounting for bubble and crash episodes leads to more accurate volatility forecasts than standard GARCH benchmarks. For Value-at-Risk and Expected Shortfall, the bubbleâcrash specifications can improve tail risk forecasting at several tail probability levels through more accurate coverage, lower quantile loss, and stronger ESR backtesting performance. The results also reveal different degrees of price exuberance across the two asset categories: while extreme price dynamics are more evident among traditional cryptocurrencies, deviations from fundamentals are rare for stablecoins. Among stablecoins, USDT exhibits limited but detectable exuberance, whereas Tether Gold does not display extreme price episodes. However, when such deviations occur, as in the case of USDT, they generate significant contagion effects on major cryptocurrencies. Notably, extreme episodes originating in USDT have a stronger impacts on Bitcoin and Ethereum than the reverse spillovers from traditional cryptocurrencies to USDT. Overall, the evidence suggests that stablecoins are not merely passive instruments within the digital-asset ecosystem. Even temporary deviations from their reference values contain valuable information for risk forecasting and contagion monitoring.
The Prop Trust Verified Standard (PTVS) v1.0 Reference Architecture establishes the definitive technical specification, capability matrix, and implementation guidelines for the physical verification of tokenized Real-World Assets (RWAs) within the European regulatory framework. This document resolves the "Physical Oracle Gap" â the structural inability of Distributed Ledger Technology (DLT) systems to attest to the physical existence, structural integrity, and legal encumbrances of off-chain assets backing tokenized securities â through a deterministic four-pillar architecture: Pillar I â eIDAS 2.0 Qualified Forensic Audits: On-site inspections conducted by sworn judicial experts under Qualified Electronic Signatures (QES) per Regulation (EU) 2024/1183. Pillar II â SHA-256 Cryptographic Lineage: Canonical JSON serialization with deterministic hashing anchored in permanent registries. Pillar III â Smart Contract Circuit Breakers: The open-source PTVSClaimInjector.sol contract (MIT License) enforces automated protective actions based on PTVS Score. Pillar IV â PTCE Network: Decentralized network of Prop Trust Certified Experts with 85/15 revenue split. Institutional validation: Formal submissions to ESMA (FOI/ESMA/2026-001), EBA (FOI/EBA/2026-002), EIOPA (FOI/EIOPA/2026-003, confirmed & registered), and ECB/SSM (FOI/ECB-SSM/2026-004, ADITO portal) Application to INATBA RWA Working Group (FOI/INATBA/2026-005) Permanent registration at CERN/Zenodo, HAL/CNRS (hal-05713062v1), OSF (DOI: 10.17605/OSF.IO/7D2SJ), and U.S. Copyright Office (Cases 1-15210573311 & 1-15234961091) Open governance via the PTVS Technical Board (17 seats, W3C/ISO-inspired) Document scope: 17 pages covering architecture overview, PTVS Score methodology (0-100), Verifiable Claims lifecycle, ERC-3643/T-REX integration, regulatory alignment matrix (MiCA, Solvency II, Eurosystem, eIDAS 2.0), governance model, 20-capability prior art inventory, and comparative analysis vs. Chainlink, Proof of Reserve, IoT sensors, Big Four audits, and registry oracles. Lead Researcher: Aurelio Tamarit Blay, Certified Judicial Expert (Exp. No. 0161, Spain), ORCID: 0009-0007-5824-3602, Wikidata: Q140774713. Institutional motto: Veritas in Re · Certitudo in Code Canonical source: https://forensics-oracle.org/reference-architecture/
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Blockchain Technology Applications and Security
Digital and Cyber Forensics
Physical Unclonable Functions (PUFs) and Hardware Security
The digital transformation of agricultural supply chains requires efficient coordination among heterogeneous stakeholders and reliable information exchange across distributed logistics networks. As a key component linking agricultural production and downstream distribution, collaboration between agricultural product distribution and textile packaging enterprises has become increasingly dependent on intelligent communication and data-sharing infrastructures. This study systematically investigates the strategic management mechanisms and implementation pathways for collaborative development by integrating transaction cost economics, complex adaptive systems theory, and network effects theory. A four-dimensional management framework encompassing technological support, organizational coordination, benefit distribution, and risk prevention is established, in which entropy weightâTOPSIS is employed for strategic objective alignment, blockchain-based architectures enable trusted information sharing, Shapley value optimization supports dynamic benefit allocation, and Value-at-Risk (VaR) models facilitate quantitative risk control. The proposed framework further incorporates smart contracts and permission-controlled data interaction to improve collaboration efficiency while preserving data security. The resulting management architecture provides a quantitative and scalable solution for digital supply chain coordination and demonstrates practical value for intelligent logistics systems. Moreover, its distributed information-sharing mechanisms and network-oriented optimization strategies offer methodological references for communication-enabled industrial ecosystems, wireless sensing infrastructures, and electromagnetic information transmission environments requiring reliable multi-node coordination and secure data exchange.
Through this independent concept, the study introduces a fresh new perspective to the world of modern forensic accounting via a theory called âThe Decentralized Fraud Matrixâ (DFM). This conceptual research was developed specifically as an analytical tool to dissect the modus operandi of financial crimes in the digital-cyber eraâincluding Web3 environments, blockchain architecture, DeFi protocols, and autonomous DAO systems. The focus of the DFM theory completely breaks away from the basic assumptions of the conventional fraud triangle, which has long been overly preoccupied with measuring human emotions. Mechanically, the originality of this theory rests on the testing of three interlocking cyber indicators in the field. These three indicators include the level of opacity in an actorâs digital identity concealment; technological engineering designed to break the audit trail of fund flows; and the exploitation of loopholes in physical national sovereignty boundaries, as well as cyber âjurisdictional evasionâ tactics aimed at neutralizing the enforcement power of on-ground regulations, thereby rendering perpetrators immune to formal legal prosecution
With the rapid advancement of industrial Internet technologies and intelligent wireless sensing infrastructures, efficient data acquisition and information transmission have become fundamental to modern textile supply chain management. The integration of electromagnetic-enabled Internet of Things (IoT) devices, RFID technologies, and intelligent communication networks provides essential support for real-time financial monitoring and digital taxation services. Against this background, this paper investigates the application of intelligent finance and taxation in textile industry supply chains by proposing an integrated framework based on artificial intelligence, blockchain, cloud computing, and IoT technologies. The framework enables transparent financial management, automated tax compliance, dynamic supply chain finance, and end-to-end traceability through seamless integration of operational, financial, and logistics data. Key applications, including blockchain-based material provenance verification, AI-driven credit assessment, automated customs and tax processing, and intelligent risk management, are systematically analyzed. The proposed architecture improves supply chain transparency, operational efficiency, sustainability, and resilience while facilitating data-driven decision-making across textile production and distribution processes. Furthermore, the study demonstrates that intelligent finance and taxation can establish a unified digital ecosystem for financial governance and supply chain collaboration, providing valuable technical references for wireless industrial information acquisition, smart sensing, and communication-assisted digital management in future intelligent manufacturing environments.
C. O. Enuma, Matthias D., V.I.E. Anireh, Bennett E.O.
Abstract The increasing adoption of cloud computing and blockchain-based smart contracts has transformed digital service delivery through decentralized automation, transparency, and trusted transaction execution. However, existing smart contract frameworks continue to face challenges related to privacy preservation, secure computation, intelligent access control, execution integrity, and auditability. Most existing solutions rely on isolated privacy-preserving mechanisms, exposing sensitive information during computation and limiting scalability and overall system performance. This study developed a Model for Privacy-Preserving Smart Contract in Cloud Computing by integrating Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEE), Federated Learning (FL), Differential Privacy (DP), Autoencoder-based anomaly detection, GraphSAGE Graph Neural Networks (GNN), Proximal Policy Optimization (PPO), and Blockchain Smart Contracts within a unified architecture. The study adopted the Design Science Research Methodology (DSRM), while Object-Oriented Analysis and Design (OOAD) guided system implementation. The proposed model was evaluated using the CICIDS2017 cybersecurity benchmark dataset across privacy, security, execution integrity, auditability, scalability, computational performance, and cost efficiency. Experimental results achieved 96% privacy preservation, 94% security strength, 99% execution integrity, 98% auditability, and 90% scalability, while the Artificial Intelligence Privacy Engine attained 98.91% validation accuracy, 0.9962 ROC-AUC, 0.9490 Macro F1-score, and 0.9718 Matthews Correlation Coefficient (MCC). Comparative analysis against RBAC, ABAC, and blockchain-based frameworks demonstrated superior performance in privacy preservation, secure computation, intelligent authorization, and auditability. The proposed model provides a practical, scalable, and intelligent solution for secure smart contract execution in privacy-sensitive cloud computing environments. Keywords: Privacy-Preserving Smart Contracts, Cloud Computing, Blockchain, Zero-Knowledge Proofs, Secure Multi-Party Computation, Trusted Execution Environments, Federated Learning, Differential Privacy, Graph Neural Networks, Artificial Intelligence.
C. O. Enuma, Matthias D., V.I.E. Anireh, Bennett E.O.
Abstract Cloud computing has become the preferred platform for deploying blockchain-enabled smart contracts because of its scalability and flexibility. However, existing access control mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and conventional blockchain authentication expose sensitive user information during authentication, rely on static authorization policies, and lack intelligent mechanisms for detecting evolving cyber threats. This study proposes an Intelligent Privacy-Preserving Access Control Framework for Cloud-Based Smart Contracts that integrates Modified Groth16 Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEE), Federated Learning, Differential Privacy, GraphSAGE Graph Neural Networks, Autoencoder-based anomaly detection, Proximal Policy Optimization (PPO), and Blockchain Smart Contracts. The framework enables credential-free authentication, confidential collaborative computation, adaptive authorization, intelligent threat detection, and immutable blockchain auditing without compromising user privacy. The proposed framework was implemented and evaluated using the CICIDS2017 cybersecurity dataset. Experimental results achieved 96.4% privacy preservation, 94.1% security strength, 99.0% execution integrity, 98.7% auditability, 90.3% scalability, 88.6% computational performance, 86.9% cost efficiency, 98.91% validation accuracy, 99.62% ROC-AUC, 94.90% Macro F1-Score, and an overall system fitness of 94.23%. Comparative evaluation against Hawk, Zether, Ekiden, and a Federated Learning-only IDS demonstrated superior performance across all evaluation metrics. The proposed framework therefore provides an intelligent, scalable, and privacy-preserving access control solution suitable for next-generation cloud-based smart contract systems. Keywords: Privacy-Preserving Access Control; Smart Contracts; Cloud Computing; Zero-Knowledge Proof; Secure Multi-Party Computation; Trusted Execution Environment; Federated Learning; Blockchain.
Abstract In the current digital era, the storage of electronic health records on centralized platforms presents significant integrity, privacy and security challenges. Further, access to this stored healthcare data should be quick and efficient, especially during emergencies. Blockchain and edge computing brought a great revolution in managing healthcare data by ensuring security, immutability, and decentralized data sharing with reduced latency. But, the integration of edge computing with the blockchain networks is still a gap to achieve ideal healthcare goals of data security with real-time data processing. The contribution of this work is two-fold. First, a novel deep reinforcement learning based medical data offloading scheme is proposed for offloading healthcare data to the nearby edge servers from the end users. The learning policy uses the proximal policy optimization algorithm for making the optimal offloading decision and minimizes the overall delay and energy consumption of healthcare devices and edge servers. Second, we proposed a secure, scalable, and consent-based data sharing scheme among multiple stakeholders such as patients, hospitals, doctors, healthcare research institutes etc. The EHR sharing scheme uses the AES and RSA algorithms for encryption, which ensures only authorized and consent-based access to the sensitive data stored in IPFS. The performance of the proposed offloading scheme is evaluated in terms of delay and energy consumption whereas data sharing scheme is evaluated in terms of latency and throughput using Hyperledger Besu and Hyperledger Caliper platforms. The experimental study exhibits that the proposed approach is both feasible and scalable, making it suitable for integration into the e-healthcare systems.
This study investigates the moderating role of blockchain traceability adoption in enhancing consumer engagement and purchase intention within live-streaming agricultural e-commerce platforms in China. Drawing upon the Stimulus-Organism-Response (S-O-R) framework operationalized at the aggregate market level and information asymmetry theory, this research employs longitudinal market-level time-series data spanning 2019 to 2024, utilizing hierarchical regression analysis with Hayes's conditional process framework to examine main effects, mediation mechanisms, and moderation relationships. The empirical findings reveal that platform development and information transparency exert significant positive effects on market purchase behavior, with consumer engagement serving as a partial mediating mechanism transmitting these effects. The moderation analysis demonstrates that blockchain traceability adoption significantly strengthens the relationships between platform stimuli and consumer engagement, with the information transparency pathway exhibiting substantially stronger moderation effects than the platform development pathway, demonstrating that blockchain technology functions as a selective trust-enhancing mechanism that validates quality signals rather than operating as a general platform enhancerâa distinction representing the central empirical contribution of this study. These findings extend the traditional S-O-R framework by incorporating technological infrastructure as a boundary condition shaping stimulus effectiveness at the market level, while providing practical guidance for platform operators and policymakers to prioritize blockchain traceability infrastructure investment in conjunction with transparency enhancement initiatives for promoting high-quality development of agricultural live streaming e-commerce.