1. Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19
- Author
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Jakob, Carolin E. M., Kohlmayer, Florian, Meurers, Thierry, Vehreschild, Jörg Janne, and Prasser, Fabian
- 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 - 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.
- Published
- 2020