In recent years, a new business paradigm has emerged which revolves around effectively extracting value from data. In this scope, providing a secure ecosystem for data sharing that ensures data governance and traceability is of paramount importance as it holds the potential to create new applications and services. Protecting data goes beyond restricting who can access what resource (covered by identity and Access Control): it becomes necessary to control how data are treated once accessed, which is known as data Usage Control. Data Usage Control provides a common and trustful security framework to guarantee the compliance with data governance rules and responsible use of organizations’ data by third-party entities, easing and ensuring secure data sharing in ecosystems such as Smart Cities and Industry 4.0. In this article, we present an implementation of a previously published architecture for enabling access and Usage Control in data-sharing ecosystems among multiple organizations using the FIWARE European open source platform. Additionally, we validate this implementation through a real use case in the food industry. We conclude that the proposed model, implemented using FIWARE components, provides a flexible and powerful architecture to manage Usage Control in data-sharing ecosystems.
This paper states the research done in building a Wildfire Propagation Simulation tool able to present graphically, how the fire will be propagated in case of a wildfire. The core of this tool was developed using Cellular Automata (CA) relayed on mathematical foundations for modeling the propagation and the transition rules of the CA. Also, the mathematical model was combined with a geographical information system provided by Google Maps API, allowing to conduct a simulation that considers the kind and density of vegetation in a specific zone of interest. Finally, we define and perform a full simulation providing information for managing and predicting wildfires and, the future works derived from this research.
The European electronic IDentification, Authentication and trust Services (eIDAS) regulation makes available a solution to ensure the cross-border mutual recognition of electronic IDentification (eID) mechanisms among Member States. However, the basic set of attributes currently provided by each country only contains citizens’ personal and legal attributes, preventing e-services to take full advantage of citizens’ domain-specific information, such as academic or medical data. In this article, we propose an extension of the eIDAS specification to support academic attributes as part of citizens’ profiles. In addition, we present an architecture to enable the connection of eIDAS nodes to national attribute providers to enrich citizens’ profiles with additional academic attributes. We have deployed the eIDAS extension in the specific case of the Spanish eIDAS infrastructure, and we have connected it to an attribute provider of the Technical University of Madrid (UPM). We have also improved a set of institutional services of that university by enabling the connection to eIDAS and enhancing the features offered to students based on their academic profiles retrieved from the eIDAS extended infrastructure. Finally, we have evaluated the resulting services thanks to real students from two different countries, concluding that the widespread adoption of the proposed solution in the academic services of European universities will greatly improve their quality and usability.
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