@inproceedings{baroud-etal-2025-beyond,
    title = "Beyond De-Identification: A Structured Approach for Defining and Detecting Indirect Identifiers in Medical Texts",
    author = {Baroud, Ibrahim  and
      Raithel, Lisa  and
      M{\"o}ller, Sebastian  and
      Roller, Roland},
    editor = "Habernal, Ivan  and
      Ghanavati, Sepideh  and
      Jain, Vijayanta  and
      Igamberdiev, Timour  and
      Wilson, Shomir",
    booktitle = "Proceedings of the Sixth Workshop on Privacy in Natural Language Processing",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.privatenlp-main.7/",
    pages = "75--85",
    ISBN = "979-8-89176-246-6",
    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."
}