Search Results (708)

Secure data collaboration among mutually distrustful organizations requires more than encrypted storage: it also needs accountable ownership control, auditable access governance, privacy-preserving transaction execution, and reliable settlement when data are exchanged as digital assets. This paper proposes TrustTrade, a unified multi-party secure data management and transaction framework designed for cross-organization data sharing, trading, and compliance-sensitive analytics. TrustTrade integrates policy-bound data capsules, a tamper-evident provenance ledger, adaptive threshold escrow, verifiable data-payment settlement, and selective audit with revocation rebinding. On four real-dataset-derived workloads, TrustTrade reaches a $90.4$–$94.8\%$ settlement rate, with a $92.5\%$ average that is $6.4$ percentage points higher than the strongest baseline average. Under adversarial request injection, TrustTrade reduces unauthorized release to $0.31\%$ and atomicity violation to $0.38\%$, corresponding to $93.6\%$ and $93.0\%$ reductions compared with Plain-Market, respectively; compared with Fixed-Escrow, unauthorized release is reduced by $77.4\%$. TrustTrade also achieves $96.7\%$ dispute-resolution accuracy while maintaining practical settlement latency. These results indicate that jointly designing secure data management and secure data transaction protocols offers a practical path toward trustworthy multi-party data ecosystems. Full article

Remote patient monitoring (RPM) is widely framed as a foundational technology for the next generation of chronic-disease care. Specific applications—pacemaker follow-up, hypertension cohorts, structured heart-failure programmes, post-surgical biosensor protocols, and virtual wards—now generate measurable clinical and economic value. Yet a decade of evaluations and implementation studies suggests that the surrounding ecosystem has matured unevenly: working applications coexist with persistent cross-cutting fragility. In this Perspective we argue that four structural gaps continue to constrain RPM’s promise at scale: (i) economic models that do not credibly compensate the asynchronous clinical work that RPM generates; (ii) ambiguous frameworks for professional liability and accountability for continuous data streams, intensified by artificial-intelligence (AI)-mediated decision support; (iii) privacy, equity, and benefit-sharing arrangements that do not yet make patients unambiguous net beneficiaries—a gap visible across very different health systems internationally; and (iv) engagement and adherence dynamics that determine whether programmes deliver value at all, but are still treated as secondary outcomes. The COVID-19 emergency briefly suspended much of the friction in this ecosystem and produced a useful natural experiment: what scaled rapidly under emergency conditions, and what subsequently atrophied, illuminates which gaps are technical, which are economic, and which are institutional. We close with a six-point research and policy agenda intended to move RPM from localised successes to a trustworthy, generalisable standard of care. Full article

The housing sector in major cities is facing escalating challenges due to rapid population growth and land scarcity. Consequently, vertical growth has been adopted as a strategic solution to optimize land use while balancing economic, social, and environmental needs. This study examines the phenomenon of vertical growth of the Dammam Metropolitan Area (DMA) in Saudi Arabia, from an urban sustainability perspective, focusing on evaluating the current state of multi-story buildings, their determinants, and their impact on quality of life and infrastructure efficiency. This study utilizes a systematic review methodology and a conceptual approach to develop an integrated framework for sustainable vertical growth. Furthermore, an empirical validation was conducted by projecting this framework onto vertical housing projects in Dammam, focusing on challenges related to design, construction quality, shared service management, and the suitability of apartments for family needs. The results indicate that the shift toward vertical growth achieves land-use efficiency, limits random horizontal expansion, and provides economic opportunities. However, it faces social and cultural constraints, most notably the resistance of some families to changing traditional ownership patterns, limited privacy and green spaces, and challenges in building maintenance and operations. The study highlights the importance of integrating urban planning, governance, architectural design, and infrastructure to ensure the sustainability of vertical growth and provide suitable housing alternatives. The study recommends further field research to assess social acceptance, improve quality-of-life indicators, and develop policies encouraging sustainable vertical expansion in alignment with Saudi Vision 2030 and the 2030 Sustainable Development Goals (SDGs), ensuring cities are more resilient, efficient, sustainable, and liveable. Full article

With the rapid development of e-commerce, data-driven models have significantly enhanced service experience. We can obtain the optimal values for the price but have also intensified consumer privacy concerns. Among various privacy protection policies, which are more effective? Is there a governance framework that balances commercial efficiency with privacy safety? To address this, we develop a duopoly game-theory model that analyzes consumer behavior characterized by heterogeneous privacy costs and preferences, aiming to evaluate the impact of differentiated privacy protection policies within digital ecosystems. We analyze whether opt-in requirement or inference regulation is more advantageous for consumer and firm competition. We find that, in a competitive environment, imposing opt-in requirement on one party can yield competitive advantages and profit increases, whereas imposing inference regulation on the other may result in a competitive disadvantage. Such differentiated policies create an asymmetric competitive landscape, effectively avoiding a prisoner’s dilemma and, under certain conditions, increasing both consumer and total surplus. Furthermore, our study reveals significant differences in the impact of these policies on data-driven and usage-driven firms. Based on these findings, we recommend that regulators carefully tailor privacy protection policies according to industry-specific data characteristics, adopting differentiated regulatory strategies when appropriate and providing compensation mechanisms for disadvantaged firms to optimize total welfare. Full article

Future 6G networks require higher performance and wider service coverage. Multi-domain Service Function Chain (SFC) deployment enables service provisioning across multiple network domains to meet these demands. However, when collaboration occurs among different network operators, privacy-preserving mechanisms are required to protect sensitive information such as internal topology and resource availability. Existing SIRM-based mechanisms, such as the Privacy-Preserving Deployment Mechanism (PPDM), address this challenge but suffer from structural limitations: PPDM performs whole-chain feasibility evaluation with extensive virtual occupation. This paper proposes a $\mathcal{B}$-Batch Sequential Deployment mechanism for privacy-preserving multi-domain SFC deployment. Instead of evaluating whole-chain feasibility at once, the proposed $\mathcal{B}$-Batch mechanism partitions each incoming SFC into fixed-size VNF batches and constructs a batch-level SIRM. This design confines virtual occupation to the current batch and reduces both its magnitude and duration while remaining fully compatible with the SIRM privacy model and the hierarchical multi-domain control architecture. A Deep Q-Network (DQN) is employed to learn substrate node selection policies based solely on SIRM-based state information, without exposing domain-internal topology or resource details. Simulation results on a three-domain AARNET substrate topology demonstrate that the proposed mechanism consistently improves deployment robustness under varying traffic intensities and SFC lengths, including short (3–6 VNFs), medium (6–9 VNFs), and long (9–12 VNFs) service chains. Compared with PPDM, the proposed $\mathcal{B}$-Batch mechanism achieves higher acceptance ratios under moderate-to-heavy traffic while reducing end-to-end delay and improving average substrate resource utilization. Node selection analysis further shows that smaller batch sizes preserve feasibility through compact node reuse, whereas larger batch sizes encourage broader substrate exploration. Overall, the proposed $\mathcal{B}$-Batch mechanism enhances feasibility preservation and deployment robustness in privacy-preserving multi-domain SFC orchestration. Full article

The increasing volume of digital health data generated through Electronic Health Records (EHRs), emergency care systems, and real-time monitoring technologies has intensified the need for secure cross-institutional healthcare analytics. However, privacy concerns, regulatory restrictions, institutional mistrust, and risks associated with centralized data aggregation continue to limit large-scale healthcare data sharing. This paper presents OPAL4Health, a governance-oriented and privacy-preserving distributed healthcare analytics framework grounded in the MIT Open Algorithms (OPAL) paradigm. The framework integrates federated analytics, blockchain-based auditability, explainable artificial intelligence (XAI), and institutional governance mechanisms within a unified computation-to-data healthcare ecosystem. Unlike conventional federated healthcare systems that primarily focus on decentralized computation alone, OPAL4Health emphasizes governance, transparency, auditability, and policy-aligned distributed analytics while preserving institutional data sovereignty. The privacy protections supported by OPAL4Health are primarily architecture-based and governance-oriented, relying on local institutional data retention, controlled query execution, and blockchain-auditable analytical workflows rather than formally provable cryptographic privacy guarantees. The framework was evaluated through a real-world urgent care pilot across seven hospitals in Riyadh, Saudi Arabia, using 184 anonymized patient cases collected between May 2015 and September 2016. Analytical findings identified a median onset-to-arrival delay of 285 min (95% Confidence Interval (CI): 270–302), low ambulance utilization (18.5%), and hospital bypass behavior in 42% of cases. Peak Emergency Department (ED) congestion periods were also identified. Scenario-based modeling projected potential long-term healthcare savings of approximately $602 million over 15 years through improved Emergency Medical Services (EMS) allocation and reduced disability-adjusted life years (DALYs). The findings demonstrate the feasibility of governance-oriented, privacy-preserving distributed healthcare analytics within OPAL4Health while generating actionable operational and policy-relevant insights without centralizing sensitive patient-level records. The proposed framework provides a transferable model for secure, transparent, and accountable digital health collaboration across healthcare ecosystems. Full article

This study aims to examine gaps in the current AML framework related to cryptocurrency and digital assets. We focused on money laundering typologies involving the conversion of illicit funds into clean value through cryptocurrency-based purchases of vouchers, gift cards, and other non-traditional instruments. We examined the existing literature on cryptocurrency and digital assets to identify gaps in detection and classification by mapping platform features and transaction pathways using an original dataset. The work adopts the Placement Layering Integration model. It conceptualises a laundering pathway that operates outside regulated intermediaries via crypto acquisition, voucher purchases on low Know Your Customer (KYC) platforms, redemption into goods, and informal resale for cash. The analysis revealed that most platforms required minimal verification for transactions, and many supported privacy coins that can hide the flow of funds from standard detection techniques. These features create conditions for cross-border money transfers that may fall outside law enforcement oversight. Such mechanisms can lead to undeclared remittance and potential tax evasion. This study contributes to the understanding of cryptocurrency related financial crime within broader money laundering typologies. It contributes to AML frameworks by identifying a shadow payment architecture, proposing targeted reforms to extend AML coverage to voucher intermediaries, and highlights areas for future research and policy improvements. Full article

As renewable penetration in data-center power supply increases, stochastic renewable output can cause tie-line power fluctuations between data centers (DCs) and the utility grid. This paper proposes a privacy-preserving multi-time-scale tie-line power smoothing method for multiple DCs. A two-stage first-order low-pass filter decomposes tie-line fluctuations into high- and low-frequency regulation targets. Server task shifting tracks the high-frequency target, while uninterruptible power supply (UPS) regulation compensates the low-frequency residual under practical energy and power constraints. Second, a federated adaptive proximal policy optimization (Fed-AdaPPO) framework is developed. Proximal policy optimization (PPO) provides stable policy optimization in the continuous action space, and the upper confidence bound (UCB)-guided adaptive exploration improves task-shifting exploration. Critically, only Critic gradients are aggregated across DCs; Actor networks, raw workload data, and user-sensitive information remain local. This design reduces the risk of exposing local state-action mappings. Results show that coordinated server-cluster and UPS regulation reduces the standard deviation of tie-line power by at least 33.4% while maintaining service quality and data privacy. Full article

The rapid integration of generative artificial intelligence (GenAI) into education across all sectors has prompted a proliferating body of scholarship addressing the ethical, social, and environmental implications of these technologies. This narrative literature review synthesises international empirical, conceptual, and policy literature published between 2022 and 2026 to trace the evolving story of ethical concerns surrounding GenAI in education. Drawing on the moral philosophy of consequentialism, particularly the utilitarian ethics of John Stuart Mill, the review analyses six interconnected domains of ethical concern: environmental sustainability and the carbon footprint of AI infrastructure; algorithmic bias, ideological encoding, and the reproduction of misinformation; user dependency and the erosion of learner agency; the displacement of critical and creative thinking; data privacy and surveillance; and the orientation of major GenAI platforms toward profit-driven and capitalistic outcomes. Unlike systematic reviews that privilege methodological replicability, this narrative review foregrounds interpretive synthesis, tracing how the ethical discourse has shifted from early alarm and prohibition toward more nuanced frameworks for responsible integration. The review identifies a consequentialist tension at the heart of the debate: while GenAI offers measurable benefits in personalisation, accessibility, and efficiency, these gains must be weighed against distributed harms that disproportionately affect vulnerable populations, the natural environment, and the epistemic foundations of education itself. The review concludes with a set of guidelines for the ethical use of GenAI in educational contexts, grounded in the literature synthesised in the article. Full article

Central Bank Digital Currencies (CBDCs) have transitioned from theoretical concepts to operational realities across multiple jurisdictions. While they promise improved payment efficiency and financial inclusion, public trust, privacy, and user adoption have emerged as the critical determinants of success. Users fear that CBDCs could enable government surveillance, while regulators require sufficient oversight to prevent illicit finance, which creates a fundamental tension between privacy and compliance. This paper addresses the question: how can policymakers craft a global policy framework for retail CBDCs that balances user privacy and trust with necessary regulatory oversight, in order to maximize public adoption? Employing a structured narrative synthesis of peer-reviewed empirical literature and case analysis of four major CBDC implementations, the Bahamas Sand Dollar, Nigeria’s eNaira, China’s e-CNY, and the proposed digital euro, the study develops a seven-component global policy framework organized across four architectural layers. We additionally formulate seven testable propositions linking each framework component to adoption and trust outcomes, providing a structured agenda for future quantitative research. Evidence from randomized survey experiments shows that strong privacy safeguards raise adoption willingness by up to 60, underscoring that privacy is not merely a civil liberty concern but a prerequisite for widespread CBDC success. The comparative cross-case assessment suggests that broader alignment with the proposed framework components appears conceptually consistent with more favorable trust and adoption patterns across the cases examined. Full article

Vehicle-to-grid (V2G) can provide flexibility and storage for low-carbon power systems while supporting sustainable mobility, yet real-world deployment remains largely confined to pilots despite substantial technical progress. This article presents a PRISMA-guided systematic review of 974 V2G/V2X studies published between 2009 and 2025 to explain why implementation lags and how it can be accelerated. Within this corpus, a total of 162 implementation-critical articles are identified and, within these, 95 studies that primarily address non-technical dimensions such as policy, markets, user behavior, and ecosystem coordination. Drawing on full-text coding, a four-domain socio-technical framework is developed that clusters recurring non-technical barriers and enablers into business–economic, governance–policy, social, and infrastructure and ecosystem domains. The analysis reveals (i) a temporal shift from technical dominance to multidisciplinary acceleration after 2021; (ii) distinct regional priorities in which Europe emphasizes regulation and business models, Asia focuses on infrastructure scaling, and the Americas on frequency services and resilience; and (iii) persistent revenue uncertainty, regulatory gaps, user resistance, and grid unreadiness as cross-cutting obstacles. For each domain, concrete transition levers and indicative deployment key performance indicators (KPIs) are derived, such as multi-actor revenue-sharing mechanisms, aggregator recognition in market rules, privacy-by-design user participation models, and targeted bidirectional charging deployment in constrained grids. Synthesizing these insights, three archetypal V2G transition pathways are proposed—regulation-led, infrastructure-first, and service-driven—that reflect regional conditions and offer alternative routes to large-scale adoption. The framework and roadmap provide researchers, policymakers, system operators, and mobility providers with an integrated basis for designing, monitoring, and evaluating V2G policies, business models, and pilots in line with energy system decarbonization goals. Full article

While Low Emission Zone (LEZ) enforcement cameras provide a constant stream of traffic data, such resources remain significantly underexploited for urban mobility planning, as their current application is restricted to enforcing vehicle access regulations and issuing fines. This paper presents MobiCugat, a framework demonstrating that Automatic Number Plate Recognition (ANPR) camera data from a municipal LEZ network can serve as the calibration backbone for high-fidelity, city-scale traffic simulations for a policy-testing Digital Twin. The case study is Sant Cugat del Vallès (Barcelona), where the local council sought to evaluate new scenarios for the area using an evidence-based, data-driven approach. Vehicle detection records from 102 LEZ ANPR cameras were processed into 15-min traffic intensity time series through a General Data Protection Regulation (GDPR)-compliant pipeline. The Realistic Urban Traffic Generator (RUTGe), a Deep Reinforcement Learning-based tool, was used to generate SUMO-compatible traffic demand whose simulated detector counts reproduce the observed camera-based intensities. The resulting simulations reproduced the observed detector-level traffic intensities with ${\mathrm{MARE}}_{\%}$ values between $2.29\%$ and $2.90\%$ across representative morning peak, midday off-peak, and evening peak traffic conditions. Additionally, camera analysis of over $470,000$ vehicle records revealed that resident traffic ($37.4\%$) dominates over through-traffic ($3.8\%$), significantly refining prior survey-based estimates. Our high-fidelity simulation tool based on SUMO, features realistic traffic patterns calibrated through AI-driven techniques, enabling the evaluation of diverse ’what-if’ scenarios—such as road closures, pedestrianization, changes in traffic direction, or relocation of bus stops. By quantifying the impact of these interventions, our tool facilitates informed decision-making prior to physical implementation. The proposed pipeline is cost-effective, privacy-preserving, and directly replicable for any municipality operating an LEZ camera network, offering a scalable template for evidence-based urban mobility planning, aligned with the European Strategy for Data and the EU Green Deal goals for sustainable mobility. Full article

Public awareness and perception of human biomonitoring (HBM) and pesticide exposure are essential for informed decision-making and policy, yet understanding remains limited and often shaped by media and advocacy. This study combined three focus group discussions with Latvian citizens and an online content analysis of pesticide-related posts. Discussions explored understanding of HBM, attitudes toward chemical exposures, and support for related research, while content analysis identified commonly discussed pesticides and the role of non-governmental organisations (NGO) in shaping public opinion. Findings indicate low awareness and frequent misconceptions about HBM, often confused with wearable health technologies rather than a tool for assessing internal chemical exposure. Concerns were mainly linked to food additives and household chemicals, with less attention to pesticides. Glyphosate emerged as the most debated pesticide, largely driven by NGO activity and media coverage. Trust in government initiatives was mixed, with concerns about political influence, industry interests, and data privacy. Nevertheless, participants expressed strong support for further national research. Overall, the results highlight gaps in public understanding and the significant influence of media and advocacy. Strengthening risk communication, transparency, and public engagement is essential to build trust and support the development of Latvia’s HBM framework. Full article

The rapid integration of AI-powered coding assistants into developer workflows has raised significant privacy and trust concerns. As developers entrust proprietary code to services like OpenAI’s GPT, Google’s Gemini, and GitHub Copilot, the unclear data handling practices of these tools create security and compliance risks. This paper addresses this challenge by introducing and applying a novel, expert-validated privacy scorecard. The methodology involves a detailed analysis of four document types—from legal policies to external audits—to score five leading assistants against 14 weighted criteria. A legal expert and a data protection officer refined these criteria and their weighting. The results reveal a distinct hierarchy of privacy protections, with a 20-point gap between the highest- and lowest-ranked tools. The analysis uncovers common industry weaknesses, including the pervasive use of opt-out consent for model training and a near-universal failure to filter secrets from user prompts proactively. The resulting scorecard provides actionable guidance for developers and organizations, enabling evidence-based tool selection. This work establishes a new benchmark for transparency and advocates for a shift towards more user-centric privacy standards in the AI industry. Full article

In multi-hazard environments, women’s social reproductive labor often constitutes a foundation of community survival, yet remains undertheorized in disaster scholarship. This study contributes to an active scholarly conversation by examining Daya Annisa, a women-led grassroots organization in Bantul Regency, Yogyakarta, Indonesia, a region under continuous geological stress from the Sunda Megathrust, the Opak Fault, and Mount Merapi. Drawing on in-depth interviews and focus group discussions analyzed through Social Reproduction Theory (SRT), with a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework reinterpreted as an analytical lens on the structural conditions of reproductive labor, the analysis traces four interlinked practices: preparedness embedded in arisan and pengajian gatherings, community-based vulnerability mapping, trust-based crisis response, and informal post-disaster livelihoods. The paper argues that resilience in such settings is best understood not as a passive capacity to absorb shocks, but as the active, gendered, and largely uncompensated labor through which communities are materially sustained when formal systems are stretched. Three policy shifts follow: long-term flexible funding calibrated to continuous reproductive preparedness; institutional integration of community-generated vulnerability data with appropriate privacy and inclusion safeguards; and inclusion of grassroots women’s organizations as autonomous decision-making actors in disaster governance. Full article