Pham Ngoc Toan, Le Tran Trung Hieu, Nguyen Vu Trung Nguyen
Carbon pricing is jurisdictional, while proof-of-work cryptocurrency mining is a highly mobile electricity load. We examine whether daily power-sector emissions display a cross-regional and distributional pattern consistent with short-run emissions displacement. Using daily observations covering calendar years 2019–2025 (with a boundary observation on 1 January 2026; N = 2550 after transformation and cleaning), we estimate quantile regressions for the EU27, the Russian Federation and the rest of the world using the interaction between Bitcoin returns and European carbon-allowance returns. The focal Russian lower-tail interaction is positive (q10 beta = 0.0662); OLS and dynamic specifications remain positive, and a 1000-replication pairs bootstrap gives p = 0.0077. The association survives a trading-day-only sample, calendar and persistence controls, and a seven-lag specification, while randomised-carbon and non-power-sector placebo outcomes are null. However, the coefficient loses conventional significance without Winsorisation, the May-2021 Chinese-ban timing prediction is not supported, and a direct EU27-minus-Russia substitution diagnostic is null. Quantile-on-quantile estimates place the largest Russian Bitcoin-return coefficients in high-carbon-price, low-emission states, but remain descriptive. Because the design does not observe mining capacity moving across jurisdictions and the available full-sample Russian emissions series is national rather than subnational, the evidence supports a leakage-consistent operational association rather than proof of physical relocation or a broad causal effect of EU carbon pricing.
Purpose To analyze shock transmission, shock absorption and systemic interconnectedness in decentralized cryptocurrency markets by examining how structural differences across cryptocurrency subcategories (green, energy and Bitcoin Gold) influence contagion dynamics and network resilience. Design/methodology/approach This study employs an R-squared decomposed connectedness approach to investigate contemporaneous and lagged spillovers among eight cryptocurrencies that have been classified as green (Cardano, XRP, Polygon and Stellar), energy-centric (Powerledger, Electrify Asia and Sun Contract) and Bitcoin Gold for the period ranging from December 31, 2019 to July 22, 2024. It evaluates directional shock transmission (“TO”), shock absorption (“FROM”) and net connectedness to identify the role of individual assets as transmitters and receivers within the network. Additionally, hedge ratios and portfolio weights are calculated to offer insights into diversification potential and hedging effectiveness across cryptocurrency subcategories. Findings The findings indicate a high degree of systemic interconnectedness among closely linked decentralized networks. Contemporaneous connectedness is more pronounced than lagged connectedness, indicating rapid information diffusion within cryptocurrency platforms. Network diffusion analysis identifies Stellar and Cardano as net transmitters (sneezers), and Sun Contract and Electrify Asia as net receivers (catchers), exhibiting systemic risk elevation and diversification capabilities, respectively. Originality/value This study contributes to Information Systems research by integrating digital contagion theory and a socio-technical perspective into the empirical analysis of cryptocurrency platforms. It introduces network-based decomposition of connectedness to differentiate between immediate and persistent contagion and offers one of the initial empirical analyses of heterogeneity across cryptocurrency subcategories. This study connects infrastructure design with contagion dynamics and provides innovative perspectives on governance-by-design, network resilience and systemic vulnerabilities in developing a digital ecosystem.
Blockchain systems provide a decentralized and fault-tolerant infrastructure for maintaining a shared transaction ledger without relying on a trusted central authority. Nakamoto consensus, in particular, enables open participation and robust agreement in permissionless environments. However, these benefits typically rely on broad data replication, which requires participants to download and propagate large volumes of transaction data and can therefore impose substantial communication overhead. This thesis proposes and analyzes a bandwidth-efficient data recoverability protocol for Nakamoto consensus using erasure-coded sampling. Instead of requiring every participant to download a full transaction batch, the protocol allows an operator to encode a large transaction batch, called a mega transaction, into coded chunks and publish a compact cryptographic commitment on chain. Participants verify sampled coded chunks and cast PoW-bound votes on their validity. The Nakamoto consensus layer then determines whether the mega transaction should be accepted as recoverable, so that it can be reconstructed and verified later if a dispute arises. The main focus of this thesis is to formalize the recoverability failure event: the event that the protocol incorrectly accepts a mega transaction as recoverable even though honest participants do not collectively hold enough valid coded chunks for reconstruction. We derive conservative analytical bounds on the probability of this event and use these bounds to formulate a utility-based parameter-selection problem under a target security requirement. Monte Carlo estimates validate the analytical bounds, and numerical results illustrate the tradeoff between recovery communication overhead and confirmation latency.
Commitment branching is a novel approach to modeling strategic interaction in multi-agent systems, particularly within the context of blockchain and decentralized autonomous organizations (DAOs). This paper introduces the concept of a state [s] that can potentially support multiple commitments, denoted as [P] and [Q]. These commitments lead to distinct computational trajectories, represented as [P → T_P] and [Q → T_Q]. The core of the model lies in the definition of B_C(s), which quantifies the number of distinct branching possibilities originating from a given intermediate state. This branching behavior directly reflects the potential for divergent strategies and the inherent complexity of decentralized decision-making. The model offers a simplified yet powerful framework for analyzing the dynamics of commitment and its impact on system evolution. Further exploration of this framework could lead to improved strategies for managing risk, optimizing resource allocation, and enhancing the robustness of decentralized systems.
Food supply chains continue to be susceptible to fraud, contamination incidents, and unclear provenance records, which erode consumer confidence and significantly harm the world economy each year. Because blockchain technology provides immutable, shareable, cryptographically verified ledgers among people who distrust each other, it is frequently suggested as a solution. The oracle problem, however, is inherited by the majority of deployed systems: a ledger ensures that recorded data is not altered, but it does not ensure that the data was accurate when it was entered. In addition to reviewing the opportunities it presents for food safety and sustainability reporting, this study examines the technological, financial, and regulatory obstacles of blockchain-based food traceability and proposes a new architecture called the Dynamic Trust-Weighted Oracle Consensus (DTW-OC) framework. We present the architecture, the scoring algorithm, a comparison against Proof-of-Work, Proof-of-Stake, and PBFT, an example dairy cold-chain scenario, and a research agenda for standardisation and interoperability. DTW-OC introduces a reputation-weighted, cross-validated oracle layer that scores every IoT sensor and human data source in real time and feeds that score into block-validator selection, so a source's influence on the ledger is proportionate to its demonstrated reliability.
PQ-Sortition is a post-quantum cryptographic sortition protocol constructed from the NTRU lattice hardness assumption and instantiated using Falcon-512 (FN-DSA). The construction uses deterministic Falcon signing to obtain a reproducible, publicly verifiable proof and combines it with a consensus-layer commit-then-reveal mechanism to address the lack of unconditional uniqueness inherent in GPV-style lattice signatures. The work introduces NTRU-Sortition, a many-time lattice-based verifiable random function construction, and provides formal analyses of third-party uniqueness, pseudorandomness under the NTRU-SIS assumption in the Random Oracle Model, and provability. The paper further defines PQ-Sortition as a post-quantum proof-of-stake leader-election protocol using a historical randomness beacon, stake-weighted sortition, adaptive difficulty, equivocation slashing, and grinding resistance. The Falcon-512 instantiation provides a 32-byte output and proofs of up to 666 bytes. The paper also presents concrete performance measurements, security parameters, consensus integration details, comparisons with prior post-quantum VRF constructions, and open research problems.
Secure financial transactions require more than just an immutable record — they also demand privacy-preserving identity assurance (which enables secure, trusted and transparent communication), adaptive fraud intelligence (to detect fraudulent transactions), policy-aware execution (so organizations can set their own rules for data use), resilient consensus (enables multiple parties to agree on data use), and auditable records within a single low-latency pipeline. Current permissioned-blockchain solutions often have independent optimizations for authentication, access control, fraud detection, consensus and auditing; as such, these separate areas lead to fragmented security decision making, unnecessary disclosure, static endorsement policies and throughput–latency tradeoffs. The research presented here describes FinTrust-X, a cross-layer risk-adaptive permissioned blockchain architecture where the security state created by each layer is used to create the next. A Zero-Knowledge Context Adaptive Role and Trust Authentication System (ZK-CARTA) provides zero knowledge context adaptive role and trust authentication to enable verifiable credentials to be selectively disclosed based on user device/session context and dynamically authorize users to minimize identity exposure and privilege abuse. Users are provided authenticated evidence to feed a Temporal Graph Transformer (TRiG-FraudFormer) that models joint transactional, account, device, merchant, beneficiary and trust relationships to produce a calibrated fraud-risk assessment along with counter-factual explanations. Risk is converted into adaptive smart contract paths, confidence levels and endorsement requirements to minimize unnecessary verification overheads. Safety constrained reinforcement learning is applied in RA-BFTune to adaptively optimize batching, ordering and Byzantine fault tolerant consensus based on transaction risk and network-states. Continuous cryptographic audit evidence is produced in PQ-AuditTwin utilizing immutable provenance, Merkle verification and ML-DSA-based post-quantum signature generations. Feedback regarding changes/drift in previous layer inputs is returned to those layers. Targeted validation results show ROC-AUC values of .96-.98 and F1 values of .92-.95 were achieved in addition to achieving authentication times less than 30ms., 1500-2000 TPS, P95 response time < 700ms, and greater than a 90% reduction in unnecessary disclosure of sensitive data from users indicating significant improvements in confidentiality, fraud-resilience, authorization-efficiency, scalability and auditability when compared against multi-organization Fabric workloads that included injected fraud and Byzantine faults.
Today, biometric authentication has become a central component of user security in social governance systems, where each government department demands access to user-specific data that varies across agencies. However, storing such data in centralized repositories increases serious privacy concerns, as unrestricted access by multiple entities maximizes the risk of data leakage. To address this, our research presents a novel biometric authentication system integrating robust privacy-preserving techniques, built on advanced deep learning architectures and differential privacy algorithms. A blockchain ledger integrated with a Merkle tree is used to securely store user identities, providing tamper-evident cryptographic validation of registered users. We further develop a novel hybrid model by integrating a pre-trained Vision Transformer (ViT) with a differential privacy-based machine learning enhanced training strategy, wherein the model is trained on noise-induced images to resist inference attacks. The system without differential privacy achieves 90.80% accuracy, 0.94 precision, 0.91 recall, and an F1-score of 0.90 in the standard configuration, while the differentially private model maintains 68.97% accuracy with ε = 6.2, ensuring a strong privacy—accuracy balance. The evaluation confirms that our proposed model, incorporating differential privacy, provides a secure and scalable solution for managing sensitive citizen data while achieving reliable performance in privacy-aware biometric verification for real-world e-governance applications.
Indonesia's national engineering accreditation system faces systemic inefficiencies, governance limitations, and data integrity challenges that hinder international recognition under the Washington Accord and impede the global mobility of engineering graduates. These shortcomings are compounded by fragmented audit trails, opaque decision-making processes, and vulnerability to credential fraud, which undermine trust in accreditation outcomes. This study aims to design a model-based system engineering framework for a blockchain-enabled accreditation system that enhances data integrity, transparency, and traceability to support evidence-based decision-making and Washington Accord compliance. A Systematic Literature Review (SLR) guided by the PRISMA protocol was conducted, synthesizing insights from 109 selected studies on blockchain implementation in accreditation and credential verification systems. The study adopts a Model-Based System Engineering (MBSE) approach using the Requirement-Functional-Logical-Physical framework to translate requirements into a structured system architecture. The proposed framework integrates Hyperledger Fabric as a permissioned blockchain network for immutable record-keeping and the InterPlanetary File System for decentralized off-chain document storage, creating a hybrid on-chain/off-chain architecture. The resulting five-layer framework comprises Participants, Digitalized Access Points, Communication, Distributed Ledgers, and Existing IT Systems layers. Key findings demonstrate that the proposed architecture significantly enhances data integrity through cryptographic verification, provides transparent and tamper-proof audit trails for all accreditation activities, enables real-time verification of document authenticity, and supports interoperability among diverse stakeholders. The framework strengthens decision-making by providing verifiable evidence for informed judgments, ensuring accountability through transparent record-keeping, and enabling cross-border trust in accreditation decisions. This research concludes that the blockchain-IPFS integrated framework offers a scalable and trustworthy pathway for transforming Indonesia's engineering accreditation system, addressing both domestic governance challenges and international compliance requirements under the Washington Accord.
This article develops a finance-oriented conceptual assessment of Sui, an object-centric Layer-1 blockchain. The analysis draws on peer-reviewed research on scalability, smart contract execution, tokenomics, decentralized finance risk, market microstructure, sustainability, and regulation. It also uses a limited set of Sui-specific academic and official technical sources to interpret protocol design. The review focuses on three features: an object-centric state model that can support parallel execution when transaction states remain sufficiently partitioned; the Move language, which uses resource-oriented semantics to constrain selected asset-handling risks; and a directed acyclic graph-based consensus pipeline intended to reduce unnecessary coordination for suitable workloads. These features are linked to finance-relevant outcomes, including execution reliability, liquidity formation, adoption persistence, market resilience, and institutional investability. The assessment remains conditional. Shared-object contention may weaken realized performance, composability may preserve important classes of smart contract risk, and token emissions may dilute the value created by ecosystem growth. Regulatory uncertainty and sustainability scrutiny also influence the institutional perimeter of the asset. The article contributes an evaluation matrix, a conceptual framework, and a set of propositions for future empirical testing. No causal or statistical inference is claimed. The central conclusion is that Sui's architecture is economically relevant only when technical performance, assurance capacity, tokenomics discipline, and institutional conditions develop together. Received: 14 April 2026 | Revised: 8 July 2026 | Accepted: 27 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Low Jun Yan: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft. Md Sharif Hassan: Methodology, Validation, Writing – review & editing, Supervision, Project administration. Nguyen Mai: Resources, Writing – original draft, Visualization.
T G Keshavamurthy, S. Guruprasad, K. N. Hareesh, M. V. Chidananda Murthy · 6 authors
In recent years, smart IoT systems pose significant challenges regarding security, scalability, and intelligent decision-making for IoT data, particularly amid advances in quantum computing attacks. Traditional cryptographic and machine learning techniques seem insufficient for real-time IoT systems where data is dynamic and large-scale and security requirements are strict. In this paper, a novel Post-Quantum Probabilistic Hidden-State Deep Learning (PQP_HS_DL) framework is presented that comprises of effective probabilistic hidden state modelling, lattice-based post-quantum cryptography and blockchain technology to process IoT data securely and efficiently. In the proposed PQP_HS_DL framework, a probabilistic hidden state model is applied to learn temporal dynamics and uncertainty in IoT data streams to provide enhanced prediction and reliable anomaly detection capabilities. A lattice-based cryptographic scheme ensures quantum-resistant security, and blockchain provides data integrity, transparency, and decentralized trusted authority management. The system is further enhanced by edge computing to alleviate latency and realize real-time processing performance. The experimental evaluation of the proposed framework is carried out under 100 IoT nodes to evaluate its performance. The results of the PQP_HS_DL provide a high classification accuracy (97.6%), and higher precision, recall, and F1-score compared to the existing techniques. The latency (72 ms) is lower, the throughput (285 transactions per second) is higher, and energy consumption (0.91) is also effective in the PQP_HS_DL framework for real-time applications of IoT. Security analysis shows that the entropy (0.98) is very high, and the attack probability (0.01) is very low. The framework uses lattice-based post-quantum cryptographic mechanisms, which are effective against any classical attacker as well as against existing quantum cryptanalytic methods, based on standard computational assumptions. Scalability analysis demonstrates that the proposed framework, evaluated with run on IoT networks with over 1000 nodes, exhibits significant performance.
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 × improvement over PoW (4 TPS) and a 32.7 × 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 patients’ 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 Argentina’s 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 Argentina’s 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 −0.54 to −1.65 (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 (−0.049, 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 (p≈0.10) 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 300–700 pixels (width) and 200–550 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 ₹18–₹25 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 challenge–response 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 verifier′s private key. The time-lock puzzle enforces a predefined delay, preventing early verification and enabling reward–penalty 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 Diffie–Hellman 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 — 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 “what-if” 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, Cronbach’s 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 auditor’s 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.