The Integrated Holistic Security and Privacy Framework Deployed in CrowdHEALTH Project

Abstract

Introduction: Individuals and healthcare providers need to trust that the EHRs are protected and that the confidentiality of their personal information is not at stake. Aim: Within CrowdHEALTH project, a security and privacy framework that ensures confidentiality, integrity, and availability of the data was developed. Methods: The CrowdHEALTH Security and Privacy framework includes Privacy Enhancing Technologies (PETs) in order to comply with the GDPR EU laws of data protection. CrowdHEALTH deploys OpenID Connect, an authentication protocol to provide flexibility, scalability, and lightweight user authentication as well as the attribute-base access control (ABAC) mechanism which supports creating efficient access control policies. Results: CrowdHEALTH integrates ABAC with OpenID Connect to build an effective and scalable base for end-users' authorization. CrowdHEALTH's security and privacy framework interacts with other CrowdHEALTH's components, for instance the Big Data Platform, that depends on user authentication and authorization. CrowdHEALTH users are able to access the CrowdHEALTH's database based on the result of an ABAC request. Moreover, due to the fact that the CrowdHEALTH system requires proofs during the interactions with data producers of low trust or low reputation level, the requirements for the Trust and Reputation Model have been identified. Conclusion: The CrowdHEALTH Integrated Holistic Security and Privacy framework meets the security criteria for an e-health cross-border system, due to the adoption of security mechanisms, such as user authentication, user authorization, access control, data anonymization, trust management and reputation modelling. The implemented framework remains to be tested to ensure its robustness and to evaluate its performance. The holistic security and privacy framework might be adapted during the project's life circle according to new legislations.

Classification Techniques for Cardio- Vascular Diseases Using Supervised Machine Learning

“…A threshold-based classifier may work well in many applications, but it may be the case that a more complicated system will perform better. It depends on the problem you are dealing with (11)(12)(13)(14)(15)(16)(17).…”

Health in All Policy Making Utilizing Big Data

“…With the analytical tools to exploit Big Data, we aim to extract dynamic knowledge from various data sources and medical records, the results of which might enable us to perform predictive risk/causal analysis, with respect to all health determinants. Therefore, using simulations and evidence-based approaches, we can support policy makers to define or tailor specific policies and contribute to HiAP (1,2,23).…”

Evidence-based Public Health Policy Models Development and Evaluation using Big Data Analytics and Web Technologies

“…Big Data Analytics can start consuming datasets and produce fine-tuned analytical and predictive models. Early detection of treatment protocols outcome and respective occurring costs are such cases (34)(35)(36)(37)(38)(39)(40)(41).…”