ArticleScientific data2020
Design and evaluation of a data anonymization pipeline to promote Open Science on COVID-19.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Quantifying the Impact of Anonymization-Induced Clinical Data Quality Loss: Methodological Quantitative Case Study Using Primary Diagnosis Codes and Hospital Length of Stay.JMIR medical informatics · 2026Article
- A case study comparing anonymized and synthetic health insurance claims data for medication safety assessments.NPJ digital medicine · 2026Article
- Enhancing pandemic surveillance and testing: a simulation modeling study utilizing german multicenter data with federated machine learning.Health care management science · 2026Article
- Knowledge and Recommendations of Stakeholders Regarding Ethical Oversight of Data Science Health Research: Protocol for a Qualitative Study.JMIR research protocols · 2025Article
- A quantitative analysis of the use of anonymization in biomedical research.NPJ digital medicine · 2025Article
- Empowering open data sharing for social good: a privacy-aware approach.Scientific data · 2025Article
- Anonymize or synthesize? Privacy-preserving methods for heart failure score analytics.European heart journal. Digital health · 2025Article
- Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.BMC medical informatics and decision making · 2024Article
- The Costs of Anonymization: Case Study Using Clinical Data.Journal of medical Internet research · 2024Article
- Utilization of anonymization techniques to create an external control arm for clinical trial data.BMC medical research methodology · 2023Observational
- Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.Health care management science · 2023Article
- Article
- Specialized palliative care for hospitalized patients with SARS-CoV-2 infection: an analysis of the LEOSS registry.Infection · 2023Article
- Age and Comorbidity Burden of Patients Critically Ill with COVID-19 Affect Both Access to and Outcome of Ventilation Therapy in Intensive Care Units.Journal of clinical medicine · 2023Article
- Disease severity in hospitalized COVID-19 patients: comparing routine surveillance with cohort data from the LEOSS study in 2020 in Germany.BMC infectious diseases · 2023Article
- Article
- A Python library to check the level of anonymity of a dataset.Scientific data · 2022Article
- Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients.Scientific data · 2022Article
- COVID-19 Severity and Thrombo-Inflammatory Response Linked to Ethnicity.Biomedicines · 2022Article
- Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.