An Evaluation of Open Source Data Anonymisation Tools for Medical Data

Medical data is inherently multimedia in nature. A single patient record typically contains diverse data types, including multimodal medical images, sensor signals, and Electronic Health Records (EHRs).

Authors

Jan Jürjens (ORCID: https://orcid.org/0000-0002-8938-0470)
Ibraheem Al-Dhamari (ORCID: https://orcid.org/0000-0002-5209-6599)
Anjali Pullattukunnel (ORCID: https://orcid.org/0009-0004-6359-2215)

Institutions

Koblenz University of Applied Sciences (DE)
Universität Koblenz (DE)

Publication Details

Published: 2025-12-06
DOI: https://doi.org/10.1145/3743093.3770973
Primary Topic: Machine Learning in Healthcare
Type: article
Field-Weighted Citation Impact: 0.00

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article

An Evaluation of Open Source Data Anonymisation Tools for Medical Data

Jan Jürjens, Ibraheem Al-Dhamari, Anjali Pullattukunnel

Dec 6, 2025

Machine Learning in Healthcare

article

An Evaluation of Open Source Data Anonymisation Tools for Medical Data

Jan Jürjens, Ibraheem Al-Dhamari, Anjali Pullattukunnel

December 6, 2025article en

Abstract

Koblenz University of Applied Sciences (DE), Universität Koblenz (DE)

Openalex Percentile: Top 79%

Machine Learning in Healthcare

DOI Open Access PDF OpenAlex

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An Evaluation of Open Source Data Anonymisation Tools for Medical Data — Jan Jürjens, Ibraheem Al-Dhamari, et al. · (2025) | TGRS Research Map | TGRS