Search Results (166)

Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment. Full article

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

This work presents a comprehensive study of entropy-based metrics for evaluating blockchain systems, focusing on on-chain ledger immutability, off-chain data integrity, and computational dynamics within blockchain virtual machines (BVMs). We develop a unified framework that models blockchain states as probabilistic distributions, quantifying uncertainty through Shannon entropy and examining its evolution under varying adversarial fractions. Extensive simulations demonstrate that on-chain entropy exhibits near-exponential decay, reflecting the cumulative reinforcement of honest consensus, while off-chain entropy remains static, highlighting the limitations of conventional data storage. Furthermore, the BVM is analyzed in terms of computation entropy, establishing its Turing completeness and demonstrating that smart-contract state evolution mirrors the information dynamics of arbitrary Turing machines. Our results provide quantitative evidence that entropy serves as both a theoretical and operational measure of immutability, tamper evidence, and protocol resilience. The proposed entropy framework offers practical tools for monitoring ledger integrity, detecting tampering, and assessing computational complexity, bridging the gap between information-theoretic principles and distributed ledger applications. This study advances both the theoretical understanding and practical evaluation of blockchain security, providing a principled methodology for analyzing distributed systems under adversarial conditions. Full article

Faculty governance in higher education depends on transparent participation, reliable quorum enforcement, accountable record keeping, and strict alignment with institutional regulations. Conventional departmental council processes provide formal authority and academic deliberation, but they often rely on manual documentation, fragmented records, and procedural enforcement that is difficult to verify after the fact. This work presents an integrated hybrid Decentralized Autonomous Organization (DAO) framework for faculty governance that combines regulatory alignment analysis, a working smart-contract prototype, and scenario-based simulation. The framework is designed for university departmental councils and is structured across three layers: off-chain community governance, on-chain protocol governance, and off-chain execution governance. It expands prior conceptual work by incorporating governance dimensions related to roles, incentives, membership, communication, decision-making, identity, auditability, conflict-of-interest handling, and institutional ratification. The evaluation simulates 1488 proposals across twelve scenarios covering four faculty sizes (15, 30, 50, and 100 members) and three adoption levels (low, moderate, and high). Scenario results indicate that adoption intensity is the dominant driver of governance performance: mean participation increases from about 33% under low usage to about 85% under high usage, quorum achievement rises from about 6% to about 96%, and execution rises from about 19% to about 70%. Relative to a modeled conventional workflow baseline, the DAO-supported process reduces decision-cycle time by about 76%, improves audit completeness by about 30%, and increases traceability from about 0.63 to 1.00. The results indicate that DAO-assisted faculty governance can strengthen transparency, procedural consistency, and auditability while preserving legally mandated university authority, but its practical value depends on sustained participation, privacy safeguards, cost control, and clearly defined hybrid control points. Full article

Successful project delivery largely depends on effective progress management to ensure schedule reliability and resource efficiency. Conventional manual and paper-based approaches remain inefficient and error-prone, often causing fragmented data and poor collaboration among stakeholders. To overcome these limitations, this study proposes a computer vision–blockchain integrated framework for closed-loop construction progress management within the Plan–Do–Check–Act (PDCA) cycle. This system supports an automated, end-to-end workflow in which UAV-captured images are processed by a computer vision model, digitally signed, and verified on a blockchain ledger, triggering smart contract-based schedule deviation alerts to relevant stakeholders. An enhanced digital signature scheme ensures data integrity during off-chain and on-chain transitions, while self-executing smart contracts coordinate schedule submissions, progress reporting, and deviation detection. Implemented on Hyperledger Fabric and validated through a case study, the framework demonstrates transparent data flow and strong performance in detection accuracy, latency, and throughput. By shifting progress management from passive reporting toward proactive control, this study provides a replicable, transparent, and tamper-resistant solution for multi-stakeholder construction progress governance. Full article

Blockchain oracles bridge on-chain smart contracts and off-chain data sources, but encrypted off-chain data still raises two practical challenges: how to verify retrieval integrity without exposing sensitive values, and how to keep verification information fresh when the off-chain data set changes. Existing oracle and outsourced-database retrieval mechanisms often rely on plaintext verification, heavy cryptographic proofs, or static authentication structures, which limits their applicability to latency-sensitive IoT and decentralized finance scenarios. To address these issues, this paper proposes a retrieval integrity verification mechanism based on CKKS approximate homomorphic encryption and an authenticated index named CKKS-Auth Tree. The proposed mechanism verifies encrypted query results through homomorphically aggregated metadata, while smart contracts record versioned verification commitments to detect stale or replayed results after updates. The scope of the mechanism is the integrity, completeness, privacy, and freshness of data after commitment and upload; verifying the physical authenticity of the original data source is outside the core threat model. Experimental results show that the proposed scheme reduces authentication and verification overhead compared with existing retrieval verification methods while supporting encrypted metadata updates and on-chain synchronization. Full article

Machine learning models increasingly rely on continuously generated sensor data for automated decision-making in Internet of Things (IoT) environments. The distributed and often insecure nature of IoT infrastructures introduces risks related to data manipulation, lack of traceability, and unverifiable model evolution. Existing solutions typically address isolated aspects such as data security or access control but do not provide end-to-end provenance across the machine learning lifecycle. This paper proposes a tamper-evident data and model provenance framework for IoT-based machine learning that integrates blockchain with off-chain storage. The framework records cryptographic hashes and metadata of data, preprocessing outputs, and trained models on-chain while maintaining large artifacts off-chain to ensure scalability. Smart contracts establish verifiable linkage among lifecycle artifacts and automate provenance registration. The framework is evaluated in a simulated IoT–ML pipeline under integrity attack scenarios including data manipulation, model tampering, and metadata modification. Experimental results demonstrate reliable detection of unauthorized modifications with low verification latency and constant on-chain storage per record under controlled conditions. These findings indicate the feasibility of hybrid blockchain architectures for tamper-evident provenance in IoT-based machine learning systems, while highlighting the need for further validation in real-world deployments. Full article

Many blockchain-based cyber threat intelligence (CTI) sharing systems emphasize immutability and auditability, but often treat CTI submissions as ordinary blockchain transactions without explicitly separating content validation from publication anchoring. This paper presents CTIB, a proof-of-concept hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) framework for CTI publication. CTIB uses a sequential workflow in which a PoS committee first evaluates CTI submissions, and an accepted feed hash is then anchored through a PoW step to provide verifiable temporal binding. The prototype is evaluated in a controlled local Hardhat environment; therefore, the results should be interpreted as prototype-level feasibility evidence rather than production-scale deployment results. CTI content is represented using STIX 2.1, canonicalized, and hashed using SHA-256; only integrity-critical evidence is stored on-chain, while full CTI content remains off-chain. Experimental results demonstrate prototype-level feasibility, with measured throughput, latency, and success rate metrics under different PoW difficulty profiles. Across ten independent local runs, CTIB achieved an average throughput between 141.13 and 166.14 feeds/min, average p50 latency between 326.18 and 403.09 ms, and average p95 latency between 553.22 and 700.82 ms under the tested difficulty profiles. Security analysis uses analytical modeling, committee capture probability, and Monte Carlo simulation to evaluate majority-attack feasibility under stated assumptions. The results indicate that sequential compromise of both validation and anchoring layers increases the cost of coordinated manipulation. Full article

With decarbonization, decentralization, and digitalization, energy coordination increasingly involves many actors, heterogeneous cyber–physical data, and compliance-sensitive settlement workflows. Although blockchain has been widely discussed in this domain, existing studies are still fragmented across application-specific or platform-specific narratives. As a result, it remains difficult to compare recurring mechanisms across scenarios or to determine which blockchain functions are operationally justified in deployable energy systems. We address that fragmentation through a structured narrative review of 41 representative sources, including prior surveys, foundational technical references, and scenario-specific studies. We formulate three research questions concerning architectural positioning, cross-scenario mechanisms, and deployment barriers. On this basis, we synthesize a unified five-layer reference architecture that links off-chain physical infrastructure and trusted data acquisition to protocol-level trust anchoring, reusable business services, interface and compliance functions, and application scenarios. The framework is then used to compare five recurring scenario families, namely peer-to-peer energy trading, carbon markets and renewable energy certificates, electric vehicle charging and vehicle-to-grid services, virtual power plants, and grid flexibility coordination. The analysis shows that blockchain is most defensibly positioned as an evidence-and-settlement trust layer, rather than as a replacement for real-time physical control. It also identifies three persistent adoption bottlenecks, namely scalable ledger interaction, trustworthy cyber–physical data binding, and interoperability with regulatory and operational infrastructures. By making the trust boundary explicit and by providing a common analytical lens for cross-scenario comparison, this review clarifies the scientific contribution of blockchain to energy systems and outlines stakeholder-oriented directions for deployable hybrid designs. Full article

The widespread deployment of Internet of Things (IoT) technologies in smart-home energy systems has increased the demand for secure, context-aware, and energy-efficient access control (AC) mechanisms. Although blockchain-based AC provides immutability, auditability, and fine-grained policy enforcement, its dependence on on-chain decision-making introduces significant computational latency and energy overhead, limiting its suitability for resource-constrained IoT environments. This paper proposes Optimized Dynamic-Attribute-Based Access Control-IoT (ODABAC-IoT), a hybrid off-chain and decentralized ABAC framework that combines off-chain LightGBM inference with selective on-chain verification to reduce blockchain workload while preserving trust and transparency. This work focuses on improving the computational efficiency, latency, and energy consumption of blockchain-enabled AC within smart-home energy systems, rather than directly optimizing physical energy consumption. In the proposed framework, high-confidence access requests are evaluated off-chain, whereas uncertain requests are forwarded to smart contracts for final validation. This hybrid decision-making strategy reduces unnecessary blockchain transactions, lowers latency, and improves computational efficiency without compromising security. Experimental results demonstrate up to 65% reduction in blockchain transaction volume, 64% improvement in latency, and 65% reduction compared to on-chain ABAC and 50% compared to hybrid blockchain approaches. These gains correspond to a reduction in daily blockchain energy consumption from 10 kWh to 3.5 kWh in a representative household scenario. The results indicate that ODABAC-IoT improves scalability, energy efficiency of the digital control layer, and responsiveness in IoT-enabled smart home energy systems, offering an effective pathway toward energy-aware and secure AC in the digital infrastructure of smart home energy systems. Full article

Passive Wi-Fi localization for critical-infrastructure security operations centers (SOCs) faces three interconnected limitations. First, many existing methods produce single-point coordinate estimates without calibrated uncertainty, making them unsuitable for automated SOC response. Second, localization pipelines often lack an evidentiary chain of custody, limiting reliable post-incident auditability. Third, SOC automation cannot safely rely on uncalibrated confidence values because erroneous high-impact actions and missed escalations carry asymmetric operational costs. This study presents a Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization framework for heterogeneous sensor networks composed of stationary sensors, mobile receivers, and UAV-assisted collection nodes. Instead of producing a single coordinate estimate, the method derives a posterior spatial distribution with calibrated uncertainty from monitor-mode observations, including RSSI aggregates, management/control frame features, channel occupancy indicators, and receiver logs. The framework combines three tightly coupled components: (i) Bayesian coordinate estimation with robust loss functions and range-dependent error modeling; (ii) uncertainty calibration that converts posterior confidence into operational SOC response modes (AUTO, VERIFY, and OBSERVE) via empirical coverage metrics and reliability diagrams; and (iii) a permissioned evidentiary logging layer that anchors integrity-relevant metadata and policy labels on-chain while keeping raw telemetry off-chain for tamper-evident auditability and scalability. The coupling between layers is explicit: calibrated confidence scores govern smart-contract gating conditions, and smart-contract policy thresholds feed back into the calibration stage. Field validation shows that localization performance degrades markedly beyond approximately 40 m, indicating a practical boundary for confident automated action. The proposed framework integrates passive sensing, uncertainty-aware localization, and blockchain-based evidentiary trust for secure critical-infrastructure sensor networks. Its key contributions are: (1) a posterior-distribution-based passive localization pipeline; (2) empirical coverage metrics for calibrating SOC response thresholds; (3) a hybrid on-chain/off-chain architecture linking localization outputs to a permissioned ledger; and (4) field validation establishing the 40 m operational validity boundary. Full article

Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions. Full article

To address statistical heterogeneity and update-level privacy risks in federated learning for geospatial data, this paper proposes a hierarchically decoupled collaborative framework that integrates client-side privacy perturbation with server-side consistency-aware aggregation, while incorporating governance as a system-level support module. Under strong non-IID conditions, the proposed soft-weight aggregation strategy mitigates update mismatch and improves convergence stability without hard filtering legitimate but distributionally shifted client contributions. Meanwhile, the risk-aware perturbation mechanism adaptively adjusts clipping and noise strength across clients to better balance privacy protection and model utility. An on-chain governance and off-chain training coordination mechanism is further introduced to support auditable and traceable collaboration without interfering with the main optimization process. Experimental results on EuroSAT_RGB with ResNet-18 show that the proposed design achieves more stable training and better overall performance than the compared baselines, especially under severe heterogeneity. These findings highlight the value of jointly considering privacy-aware perturbation and consistency-aware aggregation for improving training stability and preserving utility in geospatial federated learning under statistically heterogeneous settings. Full article

Global decarbonization and the rapid growth of distributed energy resources increase the need for information-centric mechanisms that can support secure, scalable, cross-border coordination under heterogeneous technical and regulatory conditions. This paper proposes a Global Virtual Prosumer (GVP) framework that integrates IoT sensing, multi-agent coordination, and permissioned blockchain smart contracts to operationalize cross-border energy services as auditable service commitments rather than physical power exchange. Building on prior work that validated MAS-based power management and blockchain-secured operation within individual Virtual Prosumers, the present contribution lies in the cross-border coordination layer and its associated contractual and evaluation mechanisms, not in the constituent technologies themselves. A layered IoT–AI–blockchain architecture is introduced, where off-chain optimization produces allocations and admissibility indicators and on-chain contracts enforce identity, feasibility guards, delegation and partner-assignment rules, oracle verification, and settlement time compliance outcomes. The contractual lifecycle is formalized through four smart-contract algorithms covering trade registration, conditional delegation, cooperative fulfillment, and cross-border settlement with explicit failure semantics and event-based audit trails. The framework is evaluated on a global case study with seven Virtual Prosumers and quantified using contract-centric KPIs that capture registration time rejections, settlement success versus non-compliance, oracle-driven failure attribution, and full lifecycle traceability. The results demonstrate internal consistency of the proposed lifecycle and the practical value of KPI-driven accountability for cross-border energy service coordination. At the same time, the evaluation is based on synthetic parameterization and an emulated contract environment; realistic deployment constraints—including consensus latency, cross-region communication reliability, and regulatory overlap—are discussed as explicit limitations and directions for future empirical validation. Full article

Smart contracts raise persistent challenges regarding compliance with traditional contract formalities, including writing, signature, notarization, and in certain transactions, registration. These issues are particularly significant in high-value and public-facing transactions such as real estate, where formalities determine legal validity, evidentiary sufficiency and publicity effects. While existing scholarly work has examined these challenges from either doctrinal or technological perspectives, limited attention has been given to how the functional roles of formalities interact with blockchain architecture, practitioner perceptions and institutional legal frameworks. This study addresses this gap through a mixed-methods approach combining doctrinal legal analysis with qualitative socio-legal research based on 27 semi-structured interviews with legal professionals including attorneys, judges, and academic scholars. The analysis is grounded in a civil law framework, with particular reference to the Jordanian legal system, while references to the European Union’s eIDAS Regulation are used illustratively to demonstrate regulatory approaches to digital authentication. The findings demonstrate that blockchain-based systems can effectively support the evidentiary and attribution functions of contractual formalities through cryptographic verification, consensus mechanisms, and automated execution. However, they do not independently satisfy formalities that perform cautionary, constitutive, protective or public order function, namely notarization and registration, which remain dependent on institutional validation and legal recognition. The analysis further shows that practitioner concerns reflect not only doctrinal constraints but also institutional roles and varying levels of technical familiarity. To address these limitations, the study proposes a function-based analytical framework for evaluating smart contract formalities and identifies two complementary pathways for legal adaptation: (i) institutional integration, including registry-linkage systems and hybrid contracts; and (ii) technological adaptation, including digital authentication frameworks and legal oracles that connect on-chain execution to off-chain legal conditions. The study concludes that smart contract formalities’ challenges arise not solely from technological limitations, but from the interaction between legal doctrine, institutional structures, and system design. It advances a functional framework for aligning automation with the evidentiary, protective, and publicity functions of contractual formalities. Full article