Artikel

Data classification for secure mobile health data collection systems

Data collected in Mobile Health Data Collections Systems (MHDCS) are diverse, both in terms of type and value. This calls for different data protection measures to meet security goals of confidentiality, integrity, and availability. The majority of commonly used open-source MHDCS track and monitor individuals over a while. It is therefore important to have sensitive data defined and proper security measures identified. We propose a data classification model as a basis for secure design and implementation. Our method combines interviews with case studies. The case studies focused on three of the widely used MHDCS platforms in low-resource settings; that is Muzima, Open Data Kit (ODK), and District Health Information Software (DHIS) 2 Tracker Capture. Interviews with domain experts helped define the sensitivity of data in MHDCS. The proposed data classification model provides for three sensitivity levels: public, confidential, and critical. The model uses context information and multiple parameters as inputs to a classification scheme that maps data to sensitivity levels. The generated data classifications are intended to guide developers and users to build security into MHDCS starting from the early stages of the software development life cycle.

Sprache
Englisch

Erschienen in
Journal: Development Engineering ; ISSN: 2352-7285 ; Volume: 5 ; Year: 2020 ; Pages: 1-8 ; Amsterdam: Elsevier

Klassifikation
Wirtschaft
Thema
Confidentiality
Data classification
Data collection systems
Data sensitivity
Mobile health
Security

Ereignis
Geistige Schöpfung
(wer)
Katarahweire, Marriette
Bainomugisha, Engineer
Mughal, Khalid A.
Ereignis
Veröffentlichung
(wer)
Elsevier
(wo)
Amsterdam
(wann)
2020

DOI
doi:10.1016/j.deveng.2020.100054
Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Katarahweire, Marriette
  • Bainomugisha, Engineer
  • Mughal, Khalid A.
  • Elsevier

Entstanden

  • 2020

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