Back to Search
Start Over
Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19
- Source :
- Scientific Data, Vol 7, Iss 1, Pp 1-10 (2020), Scientific Data
- Publication Year :
- 2020
- Publisher :
- Nature Portfolio, 2020.
-
Abstract
- The Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) is a European registry for studying the epidemiology and clinical course of COVID-19. To support evidence-generation at the rapid pace required in a pandemic, LEOSS follows an Open Science approach, making data available to the public in real-time. To protect patient privacy, quantitative anonymization procedures are used to protect the continuously published data stream consisting of 16 variables on the course and therapy of COVID-19 from singling out, inference and linkage attacks. We investigated the bias introduced by this process and found that it has very little impact on the quality of output data. Current laws do not specify requirements for the application of formal anonymization methods, there is a lack of guidelines with clear recommendations and few real-world applications of quantitative anonymization procedures have been described in the literature. We therefore believe that our work can help others with developing urgently needed anonymization pipelines for their projects.
- Subjects :
- Adult
Male
Statistics and Probability
Biomedical Research
Epidemiology
Science
Datasets as Topic
Library and Information Sciences
Data publication and archiving
Article
Education
Data Anonymization
Humans
Registries
Pandemics
Aged
Aged, 80 and over
COVID-19
Middle Aged
Computer Science Applications
Female
Statistics, Probability and Uncertainty
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
Software
Confidentiality
Information Systems
Subjects
Details
- Language :
- English
- ISSN :
- 20524463
- Volume :
- 7
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Scientific Data
- Accession number :
- edsair.doi.dedup.....941e396d93ef68ff4ca94c5f7b896280