Trust is a crucial factor in multi-actor data-sharing initiatives, particularly in sensitive domains like healthcare, where patient privacy, regulatory requirements, and organizational collaboration intersect. However, achieving trust-by-design, creating trust through intentional design choices, is challenging. To address this challenge, this paper investigates how trust frameworks in healthcare data-sharing are designed and how they evolve over time. Central to this inquiry is the conceptualization of “trust anchors”– designable components that provide a foundation for creating trust. Drawing on Technological Innovative Systems theory, this research qualitatively examines two healthcare trust frameworks, each at different lifecycle stages. The case studies reveal how trust anchors contribute to both the development and active management of trust frameworks. The contribution includes a lifecycle approach for trust frameworks and a matrix for categorizing trust anchors, providing guidance for organizations aiming to implement and maintain multi-actor data-sharing frameworks. We find that enforceable trust anchors are more important in the mature phase of a trust frameworks, while in the growing phase, less designable and enforceable trust factors assume a greater role.

Purpose – Although there is much research about Open Government Data (OGD), comprehensive evaluations of OGD policy impacts remain sparse. This study addresses this gap by empirically examining the alignment between OGD policy objectives and real-world outcomes across 337 Chinese municipalities. Design/methodology/approach – A typology of OGD policy objectives is developed and used to evaluate OGD policies and their impact in different geographical regions. Findings – The analysis revealed a hierarchy of outcomes where “technical support” goals yield higher impacts than “innovative value”, whereas the latter is often the goal of OGD initiatives. Regional disparities also emerge, with Eastern cities outperforming traditional industrial areas. Originality/value – These findings underscore that policy design does not guarantee expected outcomes, especially under varying regional contexts. Policy-makers should better address local characteristics and develop targeted policy strategies and effective resource allocation.

This is the 11th edition of the workshop series labeled “Web and the City - The Web and Smart Cities”, which started back in Florence in 2015 and kept on taking place every year in conjunction with the WWW conference series. Last year the workshop was held in Singapore. Each year the focus of the workshop is actualized, and this year the workshop focuses on the Responsible web and Responsible AI in cities and communities. In the era of digital twinning, AI, and the metaverse (so-called citiverse for cities), and under the UN 2030 Agenda for sustainable growth and resilience, cities are being transformed into virtual spaces that enable service automation and value generation to their communities and enterprises. Moreover, AI and web intelligence generate new types of opportunities, including generating automated transactions in these virtual spaces, which must preserve human-perceived consequences, fairness, accountability, transparency, and ethics. This workshop aims to focus on how the Web transforms cities into responsibly intelligent virtual environments.

Understanding the landscape of privacy protection in governmental systems is crucial for ensuring the trustworthiness of public services and safeguarding citizens' sensitive data from breaches or misuse. Systematic mapping and synthesis of previous research can help identify existing privacy-preserving techniques, assess their effectiveness, and highlight areas for improvement, offering valuable insights for policymakers and practitioners. We aim to conduct a systematic literature review (SLR) of privacy-preserving tools and technologies, focusing on their adoption and governments' challenges. This study also uncovers emerging trends and future research directions, contributing to developing more robust privacy strategies tailored to government needs.Given its extensive reach and government-centric methodology, this evaluation distinguishes itself from previous research. Our work methodically synthesizes privacy-preserving tools and technologies from the distinct perspective of government roles, in contrast to previous assessments that concentrate narrowly on certain technologies or areas. Our findings offer a synthesis of the government's diverse roles in privacy preservation - regulator, enforcer, user, and service provider - and address existing concerns and key privacy-related technologies. Finally, we identify significant research opportunities, such as improving privacy-preserving mechanisms to strengthen the integrity of public services and mitigate the risks of data breaches and misuse.

In response to unprecedented global urbanization, the smart city concept has emerged, leveraging ICT to enhance municipal efficiency and improve the quality of urban life. The concept of smart energy city (SEC) is closely related to smart cities, however, energy system development in a smart city context is often found eluding certain segments of society, which calls for more attention to inclusion in SEC development. In this paper, the research question is: How can inclusion be effectively integrated into a framework of SEC design? A framework is developed comprising three key principles - energy conservation, energy efficiency, and renewable energy. These principles are aligned with collaboration among stakeholders, smart energy solutions applications, and integration of these solutions. The framework is illustrated using two real-world cases of demonstration projects in the City of Amsterdam, the Netherlands. The paper concludes by presenting several strategies for fostering inclusion in SEC development. They pertain to including utilization of the framework as a guideline to promote inclusion, establishing a clear understanding of inclusion, and involving all relevant stakeholders, including citizens' rights from the project's inception, and fostering transparency regarding the objectives, interests, and individual stakeholders' value.

Despite their pivotal role in promoting transparency, open data portals often struggle to engage citizens, functioning instead as static ‘data graveyards’. While external activities, such as hackathons, can raise awareness, they do not directly cultivate sustained engagement within the portals. One promising approach to leverage citizens’ engagement motivation is the integration of game elements to transform passive data access into interactive gamified experiences. However, despite its potential, there is limited research on gamified citizens’ motivation to engage with open data portals. This paper examines how static and dynamic game elements are implemented across 31 open data portals. Lastly, we use the Self-Concordance Model to discuss the alignment between motivation, personal values, and game elements. Our findings reveal that most portals incorporate ‘discovery’ elements into their dataset-searching features, subtly gamifying exploration. Additionally, portals emphasising external activities, such as hackathons and events, often lack integrated social features, suggesting a trade-off between external engagement and sustained in-portal interaction. These findings challenge the assumption that open data engagement relies primarily on external initiatives, emphasising in-portal gamification instead. This study provides recommendations for policymakers to engage with users within open data portals.

Deploying scalable Vision Transformer applications on mobile and edge devices is constrained by limited memory and computational resources. Existing model development and deployment strategies include distributed computing and inference methods such as federated learning, split computing, collaborative inference and edge-cloud offloading mechanisms. While these strategies have deployment advantages, they fail to optimize memory usage and processing efficiency, resulting in increased energy consumption. This paper optimizes energy consumption by introducing adaptive model partitioning mechanisms and dynamic scaling methods for ViTs such as EfficientViT and TinyViT, adjusting model complexity based on the available computational resources and operating conditions. We implement energy-efficient strategies that minimize inter-layer communication for distributed machine learning across edge devices, thereby reducing energy consumption from data flow and computation. Our evaluations on a series of benchmark models show improvements, including up to a 32.6% reduction in latency and 16.6% energy savings, while maintaining mean average precision sacrifices within 2.5 to 4.5% of baseline models. These results show that our proposal is a practical approach for improving edge AI sustainability and efficiency.

Driving assist applications and connected autonomous vehicle systems are supported using AI models and algorithms, which process and analyze heavy data volumes. High-performance computing units and large memory systems support these models, algorithms, and applications, which results in additional onboard energy consumption. The current trend is also towards full electrification of vehicles and increasing connectivity in the vehicular ecosystem to support collaborative and distributed applications using vehicle-edge-cloud computing. However, with the increased focus on model performance and improving the accuracy of these models and applications, the issue of high-performance computing requirements and resulting energy consumption are overlooked. The problem becomes more challenging and complex for resource-constrained edge devices, which are battery-dependent and have limited memory and computing power. This paper proposes components for an adaptive framework to reduce energy consumption by balancing model accuracy. The contributions include proposing and integrating model partition mechanisms, adaptive deployment across edge devices and approximation strategies for the models. By integrating these components, this framework supports energy-aware development across various platforms. The approach offers a sustainable method for computing and communication-oriented applications within the vehicular ecosystem.