Search Results (765)

Addressing the challenges of privacy leakage, fragmented data silos, and high computational overhead in traditional ciphertext-policy attribute-based encryption (CP-ABE) for medical data sharing, this paper proposes an improved CP-ABE framework with outsourced decryption, integrated with consortium blockchain and the InterPlanetary File System (IPFS). The framework introduces a medical-scenario-adapted CP-ABE architecture based on a lightweight FAME design, optimizing attribute key generation and transformation key design to accommodate resource-constrained medical terminals. A hybrid encryption system is employed, combining symmetric encryption for high-efficiency processing of large medical data and CP-ABE for fine-grained access control of symmetric keys. To reduce user computational burden, a proxy-assisted secure decryption architecture is implemented, where the proxy server handles most decryption tasks while ensuring resistance to malicious proxy behavior. Furthermore, the framework provides rigorous formal security verification, achieving IND-CPA security and resilience against collusion and malicious proxy attacks. Comprehensive performance evaluations demonstrate significant improvements in key generation, encryption, and decryption efficiency, offering a better balance between security and efficiency for practical medical data sharing applications. Full article

Australia’s ban on social media for under-16s, introduced in December 2025, made it the first country worldwide to implement a nationwide prohibition on major platforms for adolescents. This narrative literature review compares Australia’s age-based restriction with international approaches to protecting young people from online risks. The review synthesized 26 academic studies and 15 grey literature sources (policy documents, legislation, and official reports published between 2015 and 2025). It employed Bronfenbrenner’s bioecological systems theory to examine effects across family, platform, institutional, and broader socio-legal contexts. Three key themes emerged: (A) Empirical findings on age-threshold policies remain inconclusive and context-dependent. While unregulated use relates to psychological vulnerabilities, structured and intentional engagement can promote social connection, identity exploration, and support access, especially for marginalized youth. (B) Global responses vary, favoring alternatives like parental consent, platform duty-of-care obligations, and screen-time control measures. (C) Balanced, sustainable harm reduction depends on combining parental involvement, platform accountability, and digital literacy education. Overall, while Australia’s precautionary approach addresses legitimate developmental and public health concerns, its effectiveness seems limited by enforcement challenges, risks of digital exclusion, and potential human rights issues. Bronfenbrenner’s framework underscores the need for coordinated governance across interconnected systems to lessen online harm. Full article

The Fifth Industrial Revolution (Industry 5.0) foregrounds human–machine collaboration, sustainability, and resilience as organizing principles for next-generation cyber-physical systems. Yet the identity and access management (IAM) architectures inherited from Industry 4.0 remain perimeter-centric, policy-static, and blind to the behavioral dynamics of human–AI teaming. This paper introduces the Human-Centric Zero Trust Identity Architecture (HC-ZTIA), a novel framework that repositions identity as the adaptive control plane for Industry 5.0 environments. HC-ZTIA integrates three mutually reinforcing innovations: (1) a Joint Embedding Predictive Architecture (JEPA)-driven Behavioral Identity Assurance Engine (BIAE) that learns abstract world models of operator and machine-agent behavior to perform continuous, context-aware identity verification without relying on raw biometric surveillance; (2) a Privacy-Preserving Adaptive Authorization Protocol (PP-AAP) employing zero-knowledge proofs and federated policy evaluation to enforce least-privilege access across human, non-human, and hybrid identity classes while satisfying data-minimization mandates; and (3) a Resilience-Oriented Trust Degradation Model (RO-TDM) that provides formally verified fail-safe identity governance under adversarial, degraded, or disconnected operating conditions characteristic of operational technology (OT) and critical infrastructure. The framework is grounded in the Agile-Infused Design Science Research Methodology (A-DSRM) and formally extends National Institute of Standards and Technology (NIST) SP 800-207 and the Cybersecurity and Infrastructure Security Agency (CISA) Zero Trust Maturity Model by addressing five identified gaps in human-centric identity governance. Simulation results, validated through Monte Carlo trials with 95% confidence intervals, provide preliminary evidence that HC-ZTIA reduces identity-related breach exposure by 73.2% (±4.1%) while maintaining sub-200 ms authorization latency under the simulated conditions, offering a principled bridge between Zero Trust rigor and Industry 5.0 human-centricity. 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

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs. Full article

Firewall policy misconfigurations remain a major source of security vulnerabilities in modern networks, particularly as firewall rule sets grow in size and complexity. Such misconfigurations, commonly referred to as firewall anomalies, can lead to unintended access control behavior and undermine network security. In this paper, we propose a formal logic rule-based framework for the systematic detection and investigation of firewall anomalies, supported by knowledge graph-based visualization. First-order logic (FOL) is employed to precisely model firewall rules and to define major anomaly types, including shadowing, redundancy, correlation, generalization, and irrelevance, in both single and distributed firewall environments. The proposed framework introduces explicit and comprehensive logical definitions for each anomaly type, enabling deterministic, interpretable, and complete detection of rule conflicts and overlaps. Complex anomalies, particularly correlation and generalization, are systematically decomposed into well-defined logical cases to facilitate the accurate identification of subtle, order-dependent interactions among firewall rules. To enhance usability and analysis, firewall rules and detected anomalies are represented using Neo4j knowledge graphs, providing intuitive visual insights into rule relationships and anomaly causes. The effectiveness of the proposed approach is validated using a real operational backbone network dataset collected from Stanford University’s campus network. Experimental results demonstrate the framework’s ability to accurately detect both simple and complex firewall anomalies under realistic network conditions. To further validate the proposed logic rules, a machine learning-based evaluation was conducted. The findings confirm their effectiveness in accurately characterizing firewall anomalies. Unlike machine learning or heuristic-based methods, the proposed approach does not require training data and guarantees formal correctness and explainability. These features make it a robust and practical solution for firewall policy verification and network security management. Full article

In this paper, we present a system built on Hyperledger Fabric (HLF) that leverages Confidential Computing (CC) technologies to strengthen data privacy guarantees beyond those achievable through application-level mechanisms alone. While HLF natively supports data confidentiality through Private Collections (PCs), which restrict data visibility to a subset of authorized network participants, these mechanisms do not protect data at the hardware level: a privileged or compromised hosting platform can access plaintext data in memory and on the filesystem irrespective of HLF access control policies. To address this limitation, we integrate CC into HLF by adopting Intel Software Guard Extensions (SGX) in conjunction with the Gramine framework. This integration enables the execution of HLF components—peer nodes, orderers, Chaincodes and client applications—within Trusted Execution Environments (TEEs). Furthermore, to securely grant access to selected data to a trusted third-party software (TPS) external to the blockchain network, we leverage the Remote Attestation (RA) feature provided by CC, as streamlined by Gramine and enforced on a per-request basis, ensuring that only verified enclaves (or “SGX enclaves”) with expected measurements may access private data. In addition, the Sealing mechanism is employed to persistently store cryptographic material required by HLF components on the filesystem while preserving both confidentiality and integrity. Together, PCs, RA, Sealing, and enclave-based execution establish a layered privacy guarantee: PCs enforce application-level data segregation among channel participants; RA provides measurement-based access control for an external TPS; Sealing ensures that cryptographic material and blockchain state remain encrypted on the filesystem; and enclave-based execution protects data in use through hardware-level memory encryption. The proposed system has been applied and experimentally validated in a logistics use case in the Port of Genoa: benchmarks against an experimental HLF deployment demonstrate an average 95th-percentile (p95) performance overhead of approximately 1.3× attributable to SGX memory encryption and Gramine-based enclave execution, whereas an elevated memory usage footprint (33–35 GB per organization) has been measured, mainly due to the Gramine environment: this remains an open direction for future work. Full article

Unmanned aerial vehicles (UAVs) are increasingly deployed in missions that require high autonomy and reliable decision-making; however, many operational concepts still assume access to GNSS and stable communication with a human operator. In contested environments, this assumption may no longer hold because GNSS degradation, radio-frequency interference, and intentional jamming can disrupt positioning and communication, thereby reducing mission effectiveness and safety. Recent surveys show that operation in GNSS-denied environments remains a major challenge and often requires alternative perception, localization, and control strategies. In response, this article investigates a reinforcement learning (RL)-based decision-support system for the autonomous control of a quadrotor UAV in a three-dimensional simulated environment. Rather than following pre-programmed waypoints, the UAV learns a control policy through interaction with the environment and reward-driven adaptation. The proposed system is designed for mission execution under uncertainty, limited external guidance, and partial observability. Two policy-gradient approaches are implemented and compared: classical REINFORCE and Proximal Policy Optimization (PPO) with an Actor–Critic architecture. The study presents the simulation environment, state and action representation, reward formulation, staged training procedure, and comparative evaluation. The results indicate that, within the considered unseen test scenario, the PPO-based configuration achieved higher mission effectiveness than REINFORCE in the final unseen test scenario, supporting the practical relevance of structured deep reinforcement learning for UAV operation in GPS-denied and communication-constrained environments. Full article

The integration of algorithmic systems into critical digital infrastructure is no longer peripheral to governance, it is governance. As AI-mediated decisions influence credit access, clinical diagnoses, criminal risk scores, and infrastructure routing, the question of who controls these algorithms and whether that control is meaningful has become a central concern for states and institutions at every level of development. Existing frameworks, including the NIST AI Risk Management Framework, ISO/IEC 42001, and the EU AI Act, have made real progress toward structured AI governance. However, none treats digital sovereignty as a first-order goal, nor do they provide integrated cross-layer guidance applicable across the diverse institutional landscape found worldwide. From this synthesis, we develop the Integrated AI Risk Governance Framework (IARGF): a four-layer structure covering policy and regulations, institutional oversight, technical controls, and operational execution, organized around five risk categories—technical, ethical, security, systemic, and sovereignty-related. A comparative analysis with major existing frameworks highlights the IARGF’s unique contributions, especially its explicit focus on sovereignty, adaptability across different institutional capacities, and recursive feedback mechanisms that connect all four governance layers. The framework is analyzed across three domains—healthcare AI, financial services, and critical infrastructure—to demonstrate its practical utility. Results confirm that governance effectiveness is a system property, not just a feature of individual layers; that digital sovereignty is both a governance goal and a distinct risk dimension with specific technical and institutional needs; and that context-aware, capacity-scaled governance is a design requirement, not a political compromise. The IARGF is presented as a conceptual governance model based on a systematic literature review rather than an empirically validated tool, and it remains to be tested in actual organizational settings. Its main contribution is the comprehensive theoretical integration of sovereignty, institutional capacity, and inter-layer governance dynamics, rather than proven performance advantages over existing models. Future research should aim to validate this framework through longitudinal case studies, expert panels, and retrospective failure analyses. Full article

This study explores the drivers of economic growth in the BRICS economies—Brazil, Russia, India, China, and South Africa—over the period 1994–2024, focusing on the roles of digital infrastructure and financial development. Using a balanced panel, we examine how internet connectivity and access to credit shape growth, both independently and in combination, while accounting for gross fixed capital formation, urbanization, and government expenditure. Given the macro-panel structure, which exhibits heteroskedasticity, serial correlation, and cross-sectional dependence, we employ robust estimation techniques, including Driscoll–Kraay standard errors (DKSE), Feasible Generalized Least Squares (FGLS), and Panel-Corrected Standard Errors (PCSE). To capture potential heterogeneity across different growth scenarios, we further apply the Method of Moments Quantile Regression (MMQR) as a robustness check. Our findings show that both internet connectivity and financial development consistently promote economic growth across all main specifications. Importantly, the interaction between these two factors is also significant, indicating that the benefits of digital infrastructure are stronger in countries with deeper financial systems, and vice versa. Among the control variables, capital accumulation and government spending positively contribute to growth, while urbanization exhibits a negative association, reflecting the structural challenges of rapid urban expansion. MMQR results confirm that these relationships hold across low-, medium-, and high-growth periods, highlighting their broad relevance. These findings highlight the synergistic role of technological and financial development and underscore the importance of integrated policies to sustain long-term, inclusive growth in the BRICS economies. This study suggests that policymakers should adopt integrated strategies that enhance digital connectivity, deepen financial development, and support productive public investment to sustain inclusive and resilient economic growth. Full article

The proliferation of marine data presents both an opportunity for ocean governance and a challenge, contributing to fragmentation across disciplines, institutions, and sectors. Marine Spatial Data Infrastructure (MSDI) stands out as a major framework for integrating marine information. However, an integrated synthesis that combines quantitative mapping of publication patterns with qualitative analysis of thematic evolution remains absent. This study employs a two-step approach combining systematic review and bibliometric analysis of Scopus-indexed literature (2000–2024). Based on a focused corpus of 20 publications rigorously screened for explicit MSDI relevance, we examine publication trends, collaboration patterns, thematic structures, and evolutionary trajectories. Results indicate accelerating scholarly interest in MSDI, with European institutions contributing 75% of the analysed publications. Policy frameworks such as the INSPIRE Directive (Infrastructure for Spatial Information in the European Community) and the Marine Strategy Framework Directive (MSFD) emerge as key drivers of research activity. Temporal analysis of this corpus suggests a tentative five-phase evolution in MSDI research: (1) foundational technical standardisation, (2) governance model implementation, (3) semantic interoperability enhancement, (4) policy integration, and (5) advanced applications incorporating FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) principles and Artificial Intelligence (AI). These phases, derived from systematic coding of thematic focus across publications, represent observed patterns within the analysed literature rather than definitive stages. This paper concludes that MSDI is moving toward a more socio-technical approach that requires the consideration of a technical-focused tool in present-day ocean governance. Future work should combine semantic AI, decentralised architectures, polycentric governance models, and impact assessment frameworks to align MSDI development with the objectives of equity, inclusion, and sustainability. Full article

We estimate the causal impact of a high-frequency bus upgrade on neighborhood labor-market outcomes using the August 2019 launch of Pace’s Pulse Milwaukee Line in the Chicago region. We use public data-Pace GTFS schedules (stops/headways), ACS tract-level socioeconomic measures, and LEHD/LODES workplace counts. Using this database, we build a tract-level panel combining annual workplace employment outcomes with multi-year household outcomes, and then we implement a transparent difference-in-differences design that compares tracts within 0.5 miles of new Pulse stops to a 0.5–2 mile control ring before and after service begins. We find no detectable short-run effects, but we estimate a positive and economically sizable increase in workplace jobs per resident ($0.066;\approx 14\%$ of the pre-treatment mean). Under conventional tract-clustered inference, this estimate is marginal (p = 0.073); thus, we interpret it as suggestive rather than definitive evidence. Our results are highly robust. Event-study estimates show flat pre-trends and post-treatment gains persisting into years +1 and +2; our placebo corridors yield null effects; and our buffer-width tests show monotonic strengthening. Finally, our population-weighted estimates remain positive, though smaller. To conclude, the results suggest that frequency improvements can reallocate jobs toward upgraded corridors even when resident employment and incomes do not move immediately. Our results may highlight a likely sequencing of impacts and the potential need for complementary land-use and workforce policies to translate accessibility gains into household-level benefits. Full article

Urban areas across Europe are undergoing rapid morphological transformations driven by densification, redevelopment, and infrastructure expansion. Monitoring these urban changes requires operational, harmonized, and reproducible approaches grounded in Earth Observation. This study presents a Copernicus use case demonstrating how the High-Resolution Layer Imperviousness Change (2015–2018) and Urban Atlas datasets can be integrated with the Guidos Toolbox (GTB) to quantify structural urban change across six metropolitan areas (Milan, Sofia, Riga, Warsaw, Viseu, Santander). Morphological Spatial Pattern Analysis (MSPA) and entropy-based indicators were applied to characterize land take, fragmentation, compaction, and internal reorganization of impervious surfaces. The combined framework captured both configurational morphology and spatial disorder, revealing divergent development patterns: pronounced heterogeneity and fragmentation in Sofia, stabilization or compact growth in Milan, Warsaw, and Santander, controlled densification in Riga, and localized intensification without outward expansion in Viseu. All analyses rely on openly accessible Copernicus data and open-source tools, ensuring full reproducibility and transferability. Outputs were disseminated through a FAIR-compliant geoportal developed within a Copernicus FPCUP project, supporting transparency and reuse. The findings underscore the value of Copernicus services for operational urban monitoring and provide a scalable methodology to support European land-use policies, including the Zero Net Land Take 2050 target and the EU Soil Strategy. Full article

Pediculosis remains a public health problem among primary schoolchildren worldwide, including in Thailand. This study aimed to determine the prevalence of pediculosis and identify associated determinants among primary schoolgirls from different socio-geographic regions of Thailand to inform effective control strategies. A cross-sectional survey was conducted among 494 schoolgirls from eastern, northeastern, and southern provinces. Data on demographic characteristics, socioeconomic status, personal hygiene practices, parental knowledge and attitudes toward head lice, and school health policies were collected using questionnaires and interviews with school administrators. Univariable analyses and a generalized linear mixed model (GLMM) with school as a random effect were used to account for clustering. The overall prevalence of pediculosis was 50.81% (95% CI: 46.31–55.20), with significant variation across provinces. In univariable analysis, several factors were associated with infestation. However, after accounting for clustering, only class level (adjusted OR = 3.09; 95% CI: 1.31–7.29) and self-performed hair washing (adjusted OR = 2.93; 95% CI: 1.57–5.49) remained significantly associated with pediculosis, while other associations were attenuated. Parental knowledge was moderate, and commonly held beliefs regarding prevention and treatment varied. None of the participating schools had routine head lice screening policies. These findings indicate that pediculosis is likely influenced by both individual and school-level factors. Control efforts may benefit from coordinated school-based approaches, alongside improved access to effective treatment and targeted health education. Full article

This article investigates the housing precarity of EU migrant workers in the Dutch–German border region, focusing on the Venlo Greenport area. Drawing on documentary analysis, 28 interviews, field observations, and stakeholder engagement, it explores how local governance, market dynamics, and framing practices shape housing outcomes. While EU law guarantees free movement, housing remains excluded from the EU rights frameworks, leaving workers dependent on employer-linked or agency-controlled short-stay facilities. These arrangements—often overcrowded, surveilled, and formally temporary—become long-term solutions, producing what we term short-stay sedentarism: prolonged residence in housing designed to deny permanence. The study conceptualises the local “battleground” where municipalities, employers, housing providers, NGOs, and residents negotiate competing interests. Seven interpretive frames—nuisance/disorder, cowboys, human rights, NIMBY, shadow power, integration, and unwanted accumulation—structure these debates, legitimising certain strategies while obscuring structural deficiencies. Findings reveal that certification and enforcement, while intended to improve standards, often entrench precariousness by sustaining the short-stay model. Emerging integration-oriented policies signal a shift but remain fragile amid economic imperatives and spatial constraints. The paper argues that addressing housing precarity requires structural reforms: expanding access to regular housing, reducing employer dependency, and recognising migrant workers as long-term residents rather than temporary labour inputs. Full article