A Flexible Solution for Privilege Management and Access Control in EHR Systems

Background: Inter-organizational healthcare businesses are ruled by a huge set of policies: legal policies, organizational policies, medical policies, ethical policies, etc., which are quite static, patients policy and process, social and environmental conditions, which are highly dynamic. In the context of a business case, those different policies must be harmonized to enable privilege management and access control decisions. Objectives: The authors offer a methodology to achieve interoperability through policies harmonization in a privilege management and access control solution for EHR systems, to be later on implemented in a cancer care network using HL7 specifications. Methods: To meet the objective, the authors make use of a system-theoretical, architecture-centric, ontology-based approach to formally representing the aforementioned polices for harmonization. Results: Because of its flexibility and generality, a policy-driven RBAC model is used to formally represent all the other access control models such as MAC, DAC, RBAC, ABAC, HL7 Data Segmentation and Labeling Services. All the policies deployed in the context of an inter-organizational collaboration for cancer care can be formalized and then harmonized. Conclusions: The authors provide an implementation-independent methodology to enable policies harmonization in EHR systems. The methodology described in the paper is independent on the maturity of organizations\u2019 privilege management and access control system. Furthermore, it does not hamper organizations progressing to more advanced solutions over the time. Even dynamic policies can be harmonized at run time, allowing an advancement towards a patient-centered care

Sustainable Identity and Access Management

For today's enterprises, information technology (IT) evolved into a key success factor affecting nearly all areas of value chains. As a consequence, identity and access management (IAM) is established for centralized and structured management of digital identities together with their access to internal assets. During this effort, a centralized management platform is created, which serves as middle-ware among available software systems and human resource applications, thereby creating a unified view and enabling business-oriented management. This enables the implementation of an according level of IT-security, business process automation and the alignment to external compliance requirements. However, as IT-infrastructures evolve over time, thereby leading to continuous changes and varying demands, these developments need to be addressed within IAM in a constant manner. As IAM is designed as a cross-cutting topic between business and IT , business requirements such as restructurings need to be realized likewise. Additionally, more and more legal requirements are set in place by external authorities which affect the way digital information are to be managed. Bringing together requirements of these different stakeholders in a comprehensive way imposes high complexity for enterprises, thereby leading to high administrational effort. This leads to a situation where enterprises are in need to constantly evaluate and adapt their implemented IAM strategy and execution. Thus the dissertation at hand is devoted to provide means of aligning IAM to a more sustainable way of operation. Within information systems research, sustainability comprises the ability to meet the needs of today without hindering future developments. To achieve this, the two concepts IAM measurement and IAM policies are leveraged. Firstly, IAM measurement enables enterprises to achieve detailed information concerning the state of an IAM infrastructure. Secondly, this effort is fostered to shift IAM to a more dynamic way of operation and provide suitable recommendations concerning how to adjust different aspects of IAM in a long-term manner. During the research process, the presented approaches have been evaluated within real-world scenarios to outline their relevance and demonstrate practical applicability

Semantically-enriched and semi-Autonomous collaboration framework for the Web of Things. Design, implementation and evaluation of a multi-party collaboration framework with semantic annotation and representation of sensors in the Web of Things and a case study on disaster management

This thesis proposes a collaboration framework for the Web of Things based on the concepts of Service-oriented Architecture and integrated with semantic web technologies to offer new possibilities in terms of efficient asset management during operations requiring multi-actor collaboration. The motivation for the project comes from the rise in disasters where effective cross-organisation collaboration can increase the efficiency of critical information dissemination. Organisational boundaries of participants as well as their IT capability and trust issues hinders the deployment of a multi-party collaboration framework, thereby preventing timely dissemination of critical data. In order to tackle some of these issues, this thesis proposes a new collaboration framework consisting of a resource-based data model, resource-oriented access control mechanism and semantic technologies utilising the Semantic Sensor Network Ontology that can be used simultaneously by multiple actors without impacting each other’s networks and thus increase the efficiency of disaster management and relief operations. The generic design of the framework enables future extensions, thus enabling its exploitation across many application domains. The performance of the framework is evaluated in two areas: the capability of the access control mechanism to scale with increasing number of devices, and the capability of the semantic annotation process to increase in efficiency as more information is provided. The results demonstrate that the proposed framework is fit for purpose

Decisioning 2022 : Collaboration in knowledge discovery and decision making: Applications to sustainable agriculture

Sustainable agriculture is one of the Sustainable Development Goals (SDG) proposed by UN (United Nations), but little systematic work on Knowledge Discovery and Decision Making has been applied to it. Knowledge discovery and decision making are becoming active research areas in the last years. The era of FAIR (Findable, Accessible, Interoperable, Reusable) data science, in which linked data with a high degree of variety and different degrees of veracity can be easily correlated and put in perspective to have an empirical and scientific perception of best practices in sustainable agricultural domain. This requires combining multiple methods such as elicitation, specification, validation, technologies from semantic web, information retrieval, formal concept analysis, collaborative work, semantic interoperability, ontological matching, specification, smart contracts, and multiple decision making. Decisioning 2022 is the first workshop on Collaboration in knowledge discovery and decision making: Applications to sustainable agriculture. It has been organized by six research teams from France, Argentina, Colombia and Chile, to explore the current frontier of knowledge and applications in different areas related to knowledge discovery and decision making. The format of this workshop aims at the discussion and knowledge exchange between the academy and industry members.Laboratorio de Investigación y Formación en Informática Avanzad

Designing Data Spaces

This open access book provides a comprehensive view on data ecosystems and platform economics from methodical and technological foundations up to reports from practical implementations and applications in various industries. To this end, the book is structured in four parts: Part I “Foundations and Contexts” provides a general overview about building, running, and governing data spaces and an introduction to the IDS and GAIA-X projects. Part II “Data Space Technologies” subsequently details various implementation aspects of IDS and GAIA-X, including eg data usage control, the usage of blockchain technologies, or semantic data integration and interoperability. Next, Part III describes various “Use Cases and Data Ecosystems” from various application areas such as agriculture, healthcare, industry, energy, and mobility. Part IV eventually offers an overview of several “Solutions and Applications”, eg including products and experiences from companies like Google, SAP, Huawei, T-Systems, Innopay and many more. Overall, the book provides professionals in industry with an encompassing overview of the technological and economic aspects of data spaces, based on the International Data Spaces and Gaia-X initiatives. It presents implementations and business cases and gives an outlook to future developments. In doing so, it aims at proliferating the vision of a social data market economy based on data spaces which embrace trust and data sovereignty

A Policy-Based Management Approach to Security in Cloud Systems

In the era of service-oriented computing, ICT systems exponentially grow in their size and complexity, becoming more and more dynamic and distributed, often spanning across different geographical locations, as well as multiple ownerships and administrative domains. At the same time, complex software systems are serving an increasing number of users accessing digital resources from various locations. In these circumstances, enabling efficient and reliable access control is becoming an inherently challenging task. A representative example here is a hybrid cloud environment, where various parts of a distributed software system may be deployed locally, within a private data centre, or on a remote public cloud. Accordingly, valuable business information is expected to be transferred across these different locations, and yet to be protected from unauthorised/malicious access at all times. Even though existing access control approaches seem to provide a sufficient level of protection, they are often implemented in a rather coarse-grained and inflexible manner, such that access control policies are evaluated without taking into consideration the current locations of requested resources and requesting users. This results in a situation, when in a relatively ‘safe’ environment (e.g., a private enterprise network) unnecessarily complex and resource-consuming access control policies are put in place, and vice versa in external, potentially ‘hostile’ network locations access control enforcement is not sufficient. In these circumstances, it becomes desirable for an access control mechanism to distinguish between various network locations so as to enable differentiated, fine grained, and flexible approach to defining and enforcing access control policies for heterogeneous environments. For example, in its simplest form, more stringent and protective policies need to be in place as long as remote locations are concerned, whereas some constraints may be released as soon as data is moved back to a local secure network. Accordingly, this PhD research efforts aims to address the following research question – How to enable heterogeneous computing systems, spanning across multiple physical and logical network locations, as well as different administrative domains and ownerships, with support for location-aware access control policy enforcement, and implement a differentiated fine-grained access control depending on the current location of users and requested resources? To address this question, the presented thesis introduces the notions of ‘location’ and ‘location-awareness’ that underpin the design and implementation of a novel access control framework, which applies and enforces different access control policies, depending on the current (physical and logical) network locations of policy subjects and objects. To achieve, this the approach takes the existing access control policy language SANTA, which is based on the Interval Temporal Logic, and combines it with the Topological Logic, thereby creating a holistic solution covering both the temporal and the spatial dimensions. As demonstrated by a hypothetical case study, based on a distributed cloud-based file sharing and storage system, the proposed approach has the potential to address the outlined research challenges and advance the state of the art in the field of access control in distributed heterogeneous ICT environments

A Dynamic Risk-Based Access Control Approach: Model and Implementation

Access control (AC) refers to mechanisms and policies that restrict access to resources, thus regulating access to physical or virtual resources of an information system. AC approaches are used to represent these mechanisms and policies by which users are granted access and specific access privileges to the resources or information of the system for which AC is provided. Traditional AC approaches encompass a variety of widely used approaches, including attribute-based access control (ABAC), mandatory access control (MAC), discretionary access control (DAC) and role-based access control (RBAC). Emerging AC approaches include risk adaptive access control (RAdAC), an approach that suggests that AC can adapt depending on specific situations. However, traditional and emerging AC approaches rely on static pre-defined risk mitigation tasks and do not support the adaptation of an AC risk mitigation process (RMP). There are no provided mechanisms and automated support that allow AC approaches to construct RMPs and to adapt to provide more flexible, custom-tailored responses to specific situations in order to minimize risks. Further, although existing AC approaches can operate in several knowledge domains at once, they do not explicitly take into account the relationships among risks related to different dimensions, e.g., security, productivity. In addition, although in the real world, risks accumulate over time, existing AC approaches do not appropriately provide means for risk resolution in situations in which risks accumulate as different, dangerous tasks impact risk measures. This thesis presents the definition, the implementation, and the application through two case studies of a novel AC risk-mitigation approach that combines dynamic RMP construction and risk assessment extended to include forecasting based on multiple risk-related utilities and events; provides support for a dynamic risk assessment that depends on one or multiple risk dimensions (e.g., security and productivity); offers cumulative risk assessment in which each action of interest can impact the risk-related utilities in a dynamic way; and presents an implementation of an adaptive simulation method based on risk-related utilities and events

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical