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Publikationstyp: Working Paper – Gutachten – Studie
Titel: Preservation of individuals’ privacy in shared COVID-19 related data
Autor/-in: Sauermann, Stefan
Kanjala, Chifundo
Templ, Matthias
Austin, Claire C.
et. al: No
DOI: 10.2139/ssrn.3648430
10.21256/zhaw-22019
Umfang: 13
Erscheinungsdatum: 17-Jul-2020
Verlag / Hrsg. Institution: SSRN
ISSN: 1556-5068
Sprache: Englisch
Schlagwörter: Covid-19; Anonymisation
Fachgebiet (DDC): 005: Computerprogrammierung, Programme und Daten
Zusammenfassung: This paper provides insight into how restricted data can be incorporated in an open-be-default-by-design digital infrastructure for scientific data. We focus, in particular, on the ethical component of FAIRER (Findable, Accessible, Interoperable, Ethical, and Reproducible) data, and the pseudo-anonymization and anonymization of COVID-19 datasets to protect personally identifiable information (PII). First we consider the need for the customisation of the existing privacy preservation techniques in the context of rapid production, integration, sharing and analysis of COVID-19 data. Second, the methods for the pseudo-anonymization of direct identification variables are discussed. We also discuss different pseudo-IDs of the same person for multi-domain and multi-organization. Essentially, pseudo-anonymization and its encrypted domain specific IDs are used to successfully match data later, if required and permitted, as well as to restore the true ID (and authenticity) in individual cases of a patient's clarification.Third, we discuss application of statistical disclosure control (SDC) techniques to COVID-19 disease data. To assess and limit the risk of re-identification of individual persons in COVID-19 datasets (that are often enriched with other covariates like age, gender, nationality, etc.) to acceptable levels, the risk of successful re-identification by a combination of attribute values must be assessed and controlled. This is done using statistical disclosure control for anonymization of data. Lastly, we discuss the limitations of the proposed techniques and provide general guidelines on using disclosure risks to decide on appropriate modes for data sharing to preserve the privacy of the individuals in the datasets.
Weitere Angaben: Preprint Version
URI: https://digitalcollection.zhaw.ch/handle/11475/22019
Lizenz (gemäss Verlagsvertrag): Lizenz gemäss Verlagsvertrag
Departement: School of Engineering
Organisationseinheit: Institut für Datenanalyse und Prozessdesign (IDP)
Enthalten in den Sammlungen:Publikationen School of Engineering

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Sauermann, S., Kanjala, C., Templ, M., & Austin, C. C. (2020). Preservation of individuals’ privacy in shared COVID-19 related data. SSRN. https://doi.org/10.2139/ssrn.3648430
Sauermann, S. et al. (2020) Preservation of individuals’ privacy in shared COVID-19 related data. SSRN. Available at: https://doi.org/10.2139/ssrn.3648430.
S. Sauermann, C. Kanjala, M. Templ, and C. C. Austin, “Preservation of individuals’ privacy in shared COVID-19 related data,” SSRN, Jul. 2020. doi: 10.2139/ssrn.3648430.
SAUERMANN, Stefan, Chifundo KANJALA, Matthias TEMPL und Claire C. AUSTIN, 2020. Preservation of individuals’ privacy in shared COVID-19 related data. SSRN
Sauermann, Stefan, Chifundo Kanjala, Matthias Templ, and Claire C. Austin. 2020. “Preservation of Individuals’ Privacy in Shared COVID-19 Related Data.” SSRN. https://doi.org/10.2139/ssrn.3648430.
Sauermann, Stefan, et al. Preservation of Individuals’ Privacy in Shared COVID-19 Related Data. SSRN, 17 July 2020, https://doi.org/10.2139/ssrn.3648430.


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