Publication
Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts
Ibrahim Baroud; Lisa Raithel; Sebastian Möller; Roland Roller
In: Proceedings of the Sixth Workshop on Privacy in Natural Language Processing. Workshop on Privacy in Natural Language Processing (PrivateNLP-2025), located at NAACL, May 4, Pages 75-85, ISBN 979-8-89176-246-6, Association for Computational Linguistics, 2025.
Abstract
Sharing sensitive texts for scientific purposes requires appropriate techniques to protect the privacy of patients and healthcare personnel. Anonymizing textual data is particularly challenging due to the presence of diverse unstructured direct and indirect identifiers. To mitigate the risk of re-identification, this work introduces a schema of nine categories of indirect identifiers designed to account for different potential adversaries, including acquaintances, family members and medical staff. Using this schema, we annotate 100 MIMIC-III discharge summaries and propose baseline models for identifying indirect identifiers. We will release the annotation guidelines, annotation spans (6,199 annotations in total) and the corresponding MIMIC-III document IDs to support further research in this area.