Publication:
A Comprehensive Model for Securing Sensitive Patient Data in a Clinical Scenario

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P04-A Comprehensive Model for Securing Sensitive Patient Data in a Clinical Scenario-2023.pdf(2.7 MB)
Date
2023-11-30
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Authors
López Martínez, Antonio ; Gil Pérez, Manuel ; Ruiz-Martínez, Antonio
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Publisher
IEEE
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Ingeniería de la Información y las Comunicaciones
DOI
10.1109/ACCESS.2023.3338170
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info:eu-repo/semantics/article
Description
©<2023>. This manuscript version is made available under the CC-BY license http://creativecommons.org/licenses/ccby /4.0/ This document is the Published, version of a Published Work that appeared in final form in [IEEE Access]. To access the final edited and published work see [https://doi.org/ 10.1109/ACCESS.2023.3338170]
Abstract
The clinical environment is one of the most important sources of sensitive patient data in healthcare. These data have attracted cybercriminals who pursue the theft of this information for personal gain. Therefore, protecting these data is a critical issue. This paper focuses on an analysis of the clinical environment, presents its general ecosystem and stakeholders, and inspects the main protocols implemented between the clinical components from a security and privacy perspective. Additionally, this article defines a complete use case to describe the typical workflow within a clinical setting: the life cycle of a patient sample. Moreover, we present and categorize crucial clinical information and divide it into two sensitivity levels: High and Very Sensitive, while considering the severe risks of cybercriminal access. The threat model for the use case has also been identified, in conjunction with the use case’s security and privacy needs. This work served us as basis to develop the minimum security and privacy requirements to protect the use case. Accordingly, we have defined protection mechanisms for each sensitivity level with the enabling technologies needed to satisfy each requirement. Finally, the main challenges and future steps for the use case are presented.
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Clinical scenario , Patient data , Threat model , Threat model , Privacy , Security
Citation
IEEE Access, vol. 11, pp. 137083-137098, 2023
URI
http://hdl.handle.net/10201/138683
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