Publications in Scientific Journals:
S Sauermann, C. Kanjala, M. Templ:
"Preservation of individualsī privacy in shared COVID-19 related data";
SSRN,
http://dx.doi.org/10.2139/ssrn.3648430
(2020),
1
- 13.
English abstract:
This paper gives insight into the pseudo-anonymization and anonymization of COVID-19 data sets. First, 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 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.
To make the re-identification of individual persons of COVID-19 (that are often enriched with other covariates like age, gender, nationality, etc.) impossible, the successful re-identification by a combination of attribute values must be prevented. This is done with methods of statistical disclosure control for anonymization of data.
Keywords:
Covid19 data, Anonymization, Statistical Disclosure Control
Electronic version of the publication:
https://www.rd-alliance.org/system/files/RDA-COVID-19-Epidemiology_Data_Sharing%28v0.052_2020-06-15%29.pdf
Created from the Publication Database of the Vienna University of Technology.