The rapid growth of urbanization in India has significantly increased the demand for efficient urban infrastructure, intelligent public services, and sustainable resource management. Cities are facing numerous challenges, including traffic congestion, rising energy consumption, water scarcity, environmental pollution, inefficient waste management, and increasing pressure on healthcare and public safety systems. Conventional urban management techniques are often inadequate for handling these complex and interconnected challenges because they rely heavily on manual monitoring and reactive decision-making. The Internet of Things (IoT), combined with smart electronic systems, has emerged as a transformative technology capable of addressing these issues by enabling real-time monitoring, automation, and intelligent decision-making. IoT-based smart electronics integrate sensors, embedded processors, wireless communication technologies, cloud computing, artificial intelligence, and data analytics to create interconnected systems that continuously collect, process, and exchange information. These technologies enable city administrators to monitor infrastructure, optimize resource utilization, improve service delivery, and enhance the quality of life for citizens. In India, the Smart Cities Mission has accelerated the adoption of IoT-enabled technologies across various sectors, including transportation, energy management, water distribution, environmental monitoring, healthcare, public safety, and digital governance. Smart electronics have enabled intelligent traffic control systems, smart street lighting, smart electricity meters, connected surveillance systems, and automated waste management solutions, thereby improving operational efficiency and reducing environmental impact. Despite significant progress, several challenges remain, including cybersecurity threats, interoperability issues, data privacy concerns, high deployment costs, and the need for standardized communication protocols. This paper presents a comprehensive discussion on the role of IoT-based smart electronics in building smart cities in India. It examines the technological architecture, key applications, implementation challenges, and future opportunities associated with IoT-driven urban development. The paper concludes that the integration of IoT with emerging technologies such as artificial intelligence, edge computing, fifth-generation (5G) communication, blockchain, and digital twin technologies will play a crucial role in achieving sustainable, resilient, and citizen-centric smart cities in India.
Feeroj Nasirkhan Pathan, Amarsingh Udhavrao Solanke, Mr. Wasim Taher Khan, Dr. Mangesh Manohar Dasare
The vision of Viksit Bharat 2047 seeks to transform India into a developed, inclusive, and globally competitive nation by the centenary of its independence. Achieving this vision requires a digitally enabled financial system that promotes innovation, expands financial inclusion, and supports sustainable economic growth. In this background, Financial Technology (FinTech) has emerged as a key driver of India's digital transformation. India's FinTech ecosystem has grown quickly with the support of Digital Public Infrastructure (DPI), including Aadhaar, Pradhan Mantri Jan Dhan Yojana (PMJDY), Unified Payments Interface (UPI), DigiLocker, India Stack and e-KYC. These initiatives have expanded access to financial services, accelerated digital payments, enhanced access to formal credit, strengthened public service delivery, and encouraged wider participation in the Indian economy. Emerging technologies such as artificial intelligence, blockchain, cloud computing, big data analytics, and application programming interfaces (APIs) have additionally enhanced the efficiency and accessibility of financial services. This chapter examines the role of FinTech in advancing the vision of Viksit Bharat 2047 by promoting financial inclusion, strengthening Digital Public Infrastructure, supporting entrepreneurship, improving governance, and fostering sustainable economic development. It also examines key challenges, that influence the long-term growth of the sector. It concludes that FinTech is more than a technological innovation; it is a strategic move of India's economic transformation.
This research introduces a novel Unified Quantum-Resilient Blockchain-Zero Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF) aimed at enhancing security in IoT environments. The system combines post-quantum cryptography, blockchain technology, and Zero Trust Architecture (ZTA) to provide secure communication, access management, and privacy-preserving authentication. It uses a Deep Q-Network Multi-Factor safe Key (DQN-MFSK) for dynamic key selection, a hybrid Reinforcement-Lattice Blockchain Key Generation for quantum-resilient key creation, and Zero-Knowledge Proofs for privacy-preserving signatures to ensure a safe Internet of Things environment. Data privacy, secrecy, auditability, traceability, and resistance to changing threats, such as quantum attacks, are all guaranteed by this architecture. Transparency and thorough post-event audit trails are supported by the blockchain ledger's immutability, which records all access attempts, data exchanges, and device interactions in an unchangeable way. Through a tracing key kept on the audit server within the Zero Trust Architecture, the architecture allows accurate source tracing in the event of suspicious activity or breaches. QBC-ZKPAF provides strong security and privacy solutions for Internet of Things networks by adopting multi-factor authentication and decentralizing identity management. The framework's efficacy is confirmed by experimental results, which show 98% privacy preservation, 700 TPS throughput, 0.98 quantum resilience, and 96% access control effectiveness, making it ideal for contemporary blockchain and IoT applications.
Transparency in public affairs interactions between companies and governments is critical to democratic legitimacy, yet existing lobby registers suffer from fragmented reporting, weak record integrity, limited traceability, and compliance gaps. This paper reports a Design Science Research (DSR) study that develops and evaluates a permissioned blockchain architecture for mandatory Public Affairs transparency. Two stakeholder surveys provided empirical grounding: Survey 1 (N = 61 domain professionals) elicited functional, non-functional, and GDPR compliance requirements, while Survey 2 (N = 14 practitioner evaluators) assessed a proof-of-concept implementation on Hyperledger Fabric following a live demonstration. Findings reveal widespread concerns over non-repudiation and auditability in current systems — 87% rated existing record integrity as weak—alongside strong endorsement for blockchain’s immutability, versioned audit trails, and hybrid on-/off-chain design to ensure GDPR-aligned traceability. Post-demonstration evaluation achieved a mean score of 4.6/5 for traceability and integrity, and 86% of evaluators recommended real-world piloting. The study makes three contributions: (i) an empirically derived requirements model and information-lifecycle framework; (ii) a hybrid permissioned-blockchain blueprint implemented on Hyperledger Fabric; and (iii) a replicable stakeholder-centric DSR methodology for sociotechnical artefact design in regulated governance contexts.
Tendermint is among the most widely studied and deployed Byzantine fault-tolerant (BFT) consensus protocols, owing in part to its native leader-rotation mechanism that subsumes complex view changes. Like most partially-synchronous BFT protocols, Tendermint tolerates $f 5f$ setting that decides in two communication steps in the good case, while preserving Tendermint's leader-rotation structure. Fast Tendermint collapses Tendermint's prevote and precommit steps into a single voting step and merges the $locked$ and $valid$ state. We give proofs of agreement, validity, and termination, and a formal specification in Quint, a modern surface syntax for TLA+, used to model-check the protocol.
Zeta Avarikioti, Dimitris Karakostas, Karl Kreder, Shreekara Shastry
Our work presents a defense mechanism against Maximal Extractable Value (MEV) opportunities in distributed ledgers. The mechanism relies on the idea of enforcing a verifiable delay when generating transactions, such that a block creator cannot react to the appearance of a MEV opportunity without breaking liveness. We present positive results both in the Byzantine setting and in a game theoretic model of rational participants. We additionally present negative bounds that outline the limitations of this line of defense. Finally, we explore real-world implementation details of verifiable delays and show that, based on historical MEV data, our mechanism could realistically help prevent most existing MEV threats.
Sofia Bobadilla, Humaira Afrin, Angela Novelli, Martin Monperrus
Blockchains are among the most adversarial environments in computing. Billions are stolen by cybercriminals who exploit vulnerabilities. This is an open problem and no concept or technique has proven to really make a difference. In this paper, we claim that the classical notion of program invariant is perhaps the most powerful solution to the problem. We devise anoriginal experimental protocol to 1) study how invariants would have protected against past real-world attacks and 2) whether state-of-the-art automated tools can find them. The experimental toolchain is sophisticated. It is based on INVARIANTEVAL, a benchmark of 28 real Ethereum exploits, each paired with a human-authored invariant that blocks the attack. We validate every invariant with PONDEREPLAY, a replay framework that re-executes transactions in order to prove the correctness and soundness of smart contract invariants. We demonstrate that smart contract invariants block all the cybercriminal attacks in INVARIANTEVAL, fully validated by replaying 108,637 historical transactions. Our large-scale experiments clearly demonstrate that smart contract invariants protect against cybercriminals.
Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard to interpret. This research proposes a scalable, application-agnostic framework for \emph{persistent behavioural pattern discovery} from large-scale blockchain activity. It constructs behaviour sentences enriched with contract, token and market context, then applies a two-step embedding process: sentence-level embeddings capture individual actions, while sequence-level embeddings capture user behaviour over time. An interpretable behavioural profiler characterizes discovered communities through behavioural motifs, routines, temporal dynamics, entity exposure, and suspiciousness evidence. Evaluation on Ethereum using over 30 million transactions shows that the framework uncovers both routine and malicious behavioural patterns, including decentralised exchange (DEX) trading, NFT activity, phishing, bot operations, oracle manipulation, and rug-pull schemes. Importantly, many patterns remain stable across independent observation windows, enabling the identification of long-term behaviours beyond a single analysis period. The proposed framework combines scalability, interpretability, and persistence analysis, supporting blockchain forensic investigation, behavioural attribution, and threat discovery.
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 Crash–GARCH 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, Tether’s USD-pegged (USDT), and Tether Gold and evaluates asset-specific bubble–crash 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 bubble–crash 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" — 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 — through a deterministic four-pillar architecture: Pillar I — 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 — SHA-256 Cryptographic Lineage: Canonical JSON serialization with deterministic hashing anchored in permanent registries. Pillar III — Smart Contract Circuit Breakers: The open-source PTVSClaimInjector.sol contract (MIT License) enforces automated protective actions based on PTVS Score. Pillar IV — 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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2 source records
Blockchain Technology Applications and Security
Digital and Cyber Forensics
Physical Unclonable Functions (PUFs) and Hardware Security
El vertiginoso avance de las tecnologías de la información y la consolidación de la economía digital han conducido a la emergencia de los criptoactivos como instrumentos financieros de uso masivo, generando profundos desafíos para los sistemas tributarios actuales. El presente artículo científico estudia el contacto entre los criptoactivos y el principio de legalidad tributaria en Ecuador, adentrándose en las lagunas de la ley para la determinación, valoración y control de las rentas digitales. A través de un enfoque cualitativo de corte exploratorio y descriptivo se estudia la normativa constitucional y tributaria ecuatoriana realizando síntesis con los estándares que aparecen en la propuesta presentada por la Organización para la Cooperación y el Desarrollo Económicos (OCDE). Los hallazgos indican la ausencia de una reserva de ley que establezca de manera expresa la índole jurídica y la determinación de la cuantía de las operaciones con criptoactivos, lo que genera inseguridad jurídica, quebranta los principios de justicia tributaria y afecta la recaudación tributaria. Se delimitan cuatro áreas que se consideran decisivas en este campo: la indeterminación del hecho imponible, la dificultad en la valoración de los activos volátiles, la evasión del Impuesto a la Salida de Divisas (ISD) a través de operaciones cruzadas, y la falta de herramientas tecnológicas en la administración tributaria para realizar un seguimiento de las operaciones descentralizadas. La investigación ofrece una propuesta de armonización normativa que respete el marco constitucional ecuatoriano sin aniquilar la innovación tecnológica, concluyendo que existe la necesidad de la actualización de la ley tributaria para que incluya de manera expresa las rentas digitales en el hecho imponible, se constituyan mecanismos de valorización confiables y se apliquen tecnologías de auditoría en blockchain para el seguimiento fiscal de esta clase de operaciones.
Open access
Business, Innovation, and Economy
Economic, Educational, Environmental and Organizational Development
In recent years the growth of cloud computing, Internet of Things (IoT), artificial intelligence (AI) and edge intelligence has been increasing, and with it the need for portable, scalable and secure computing infrastructures that can process vast amounts of data that is dispersed, and has very low latency. Traditional cloud infrastructures are typically based on central server deployments which can be costly to deploy, immobile, have potentially greater communication latency, and waste resources in dynamic workload environments. In this paper, we introduced an Intelligent Portable Edge – Cloud Computing Architecture (IPECA) that combines the portable computing hardware, AI-based workload prediction, adaptive resource optimization, container-based virtualization and secure edge-cloud collaboration into a single computing architecture. In conventional architectures, there is no intelligent resource orchestration mechanism, which can provide flexible allocation of computational resources according to the property of workload, thermal status, energy consumption, network availability and so on. The architecture also features an adaptive security layer leveraging multiple layers of authentication, secure communication protocols, blockchain for integrity verification and on-the-fly system health monitoring to enhance cyber resilience. Simulations are conducted with varying workloads to gauge the effectiveness of the proposed architecture, and compared to traditional cloud and edge-cloud architectures with the metrics of latency, throughput, CPU utilization, response time, energy consumption, thermal efficiency, and resource utilization. Experiments demonstrate significant energy savings, scalability, responsiveness of the system and efficiency of computations using secure distributed processing. The suggested architecture is viable for the coming intelligent cloud infrastructures that are essential for smart city, industrial IoT, digital healthcare, education and enterprise computing.
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 weight–TOPSIS 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.
This study investigates the theoretical foundations, practical applications, and optimization strategies of financial big data analysis and network security optimization in support of Sustainable Development Goals (SDGs). A comprehensive framework is developed to integrate sustainable financial management, environmental cost-benefit analysis, socially responsible investment decision-making, and sustainable supply chain management. The study further proposes a network security optimization architecture incorporating multi-level data encryption, access control, real-time threat monitoring, intelligent defense mechanisms, and blockchain-based data protection. The proposed framework is particularly applicable to communication-intensive environments, including wireless communication infrastructures and antenna-supported information transmission networks, where secure and reliable financial data exchange is essential. Experimental analyses demonstrate that the integration of financial big data technologies and network security mechanisms enhances data protection, operational efficiency, and sustainable decision-making capabilities. The results provide a practical reference for secure financial data governance and sustainable development in complex digital and communication-oriented systems.
Through this independent concept, the study introduces a fresh new perspective to the world of modern forensic accounting via a theory called “The Decentralized Fraud Matrix” (DFM). This conceptual research was developed specifically as an analytical tool to dissect the modus operandi of financial crimes in the digital-cyber era—including 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 actor’s 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 “jurisdictional evasion” 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.
This pamphlet argues that the fiat monetary system is fundamentally incompatible with the deflationary nature of technological progress. It proposes an Energy Standard — a decentralized, blockchain-based currency backed by physically produced kilowatt-hours — as a thermodynamic anchor for money in the age of AI and robotics. Drawing on the Austrian School of Economics (Mises, Hayek), game theory, and thermodynamics, it analyses incentive structures in energy markets and makes the case for a market-driven ecological transition without state coercion.ditigal: petznek.at/pamphlet
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.
A transformação digital tem ampliado a adoção de tecnologias capazes de fortalecer a eficiência, a transparência e a confiabilidade dos processos organizacionais. Nesse contexto, este estudo propõe um modelo conceitual de integração entre a tecnologia blockchain e o Índice de Resiliência Organizacional (IRO), com o objetivo de fortalecer a produtividade, a eficiência empresarial, a governança corporativa e a confiabilidade dos indicadores utilizados na gestão organizacional. A pesquisa possui abordagem qualitativa, exploratória e descritiva, fundamentada em revisão bibliográfica e análise documental sobre blockchain, transformação digital, produtividade, eficiência organizacional, governança corporativa e resiliência organizacional. O modelo proposto incorpora o blockchain como uma camada tecnológica de confiança aplicada ao IRO, possibilitando maior integridade, autenticidade, rastreabilidade, transparência e auditabilidade dos dados utilizados na avaliação da resiliência organizacional. A integração também considera o potencial dos contratos inteligentes para automatizar procedimentos, validar evidências, atualizar indicadores e fortalecer mecanismos de controle interno. A análise demonstra que informações organizacionais mais seguras e verificáveis podem contribuir para decisões baseadas em evidências, redução de vulnerabilidades, melhoria dos processos, fortalecimento da governança e maior capacidade adaptativa das organizações. Como contribuição teórica, o estudo aproxima dois campos ainda pouco integrados na literatura: blockchain e avaliação da resiliência organizacional. Conclui-se que a integração entre blockchain e IRO constitui uma proposta inovadora para ampliar as aplicações da tecnologia blockchain na Administração e apoiar o desenvolvimento de organizações mais produtivas, eficientes, transparentes, resilientes e sustentáveis.
C. N. He, H. D. Chen, H. J. Tian, J. Zhang · 5 authors
This study investigates low-carbon investment strategies in power supply chains under the combined influence of carbon quota mechanisms (CQM) and blockchain technology (BCT). A two-echelon system consisting of a power generator and an electricity retailer is modeled, and four decision scenarios are constructed by considering blockchain adoption under both the grandfathering method (GFM) and benchmarking method (BMM). A Stackelberg game framework is employed to analyze the interactions among low-carbon technology investment, low-carbon electricity promotion, market demand, and enterprise profitability. Results show that the BMM consistently induces higher low-carbon investment levels, stronger market demand, and greater retailer profitability than the GFM, regardless of blockchain adoption. Furthermore, blockchain-enabled information traceability exhibits a significant threshold effect: when implementation costs remain below a critical level, trusted information transmission enhances consumer green trust, stimulates demand for low-carbon electricity, and improves the economic performance of supply-chain participants. Sensitivity analysis further demonstrates that consumer green trust, low-carbon preference, and responsiveness to low-carbon promotion positively influence both emissionreduction efforts and enterprise profitability, whereas excessive blockchain deployment costs weaken these benefits. The proposed framework provides a quantitative methodology for analyzing information-enabled lowcarbon decision making and coordinated investment strategies in modern power systems.
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 enhancer—a 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.