@inproceedings{paul-etal-2026-deid,
title = "{D}e{ID}-Clinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment",
author = "Paul, Angel and
Shaji, Dhivin and
Han, Lifeng and
Del-Pinto, Warren and
Nenadic, Goran and
Verberne, Suzan",
editor = {Siegert, Ingo and
Szawerna, Maria Irena and
Choukri, Khalid and
Dobnik, Simon and
Kamocki, Pawe{\l} and
Lindstr{\"o}m Tiedemann, Therese and
Lison, Pierre and
Mu{\~n}oz S{\'a}nchez, Ricardo and
Pil{\'a}n, Ildik{\'o} and
S{\"o}derg{\r{a}}rd, Lisa and
Talmoudi, Kossay and
Volodina, Elena and
Vu, Xuan-Son},
booktitle = "Proceedings of the Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy ({LEGAL}2026 and {CALD}-pseudo 2026) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA",
url = "https://aclanthology.org/2026.legal-1.5/",
doi = "10.63317/5muz36muepa2",
pages = "40--52",
abstract = "The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered framework for automated pseudonymization and re-identification risk assessment of clinical free-text data. Our approach integrates domain-adapted transformer models, including BioBERT and ClinicalBERT, into the MASK de-identification framework to improve the detection and masking of protected health information (PHI). Beyond entity recognition, we introduce a novel document-level risk assessment module that quantifies residual re-identification risk using a combination of k-anonymity, l-diversity, t-closeness, contextual similarity, and entity co-occurrence analysis. Experiments conducted on the i2b2 2014 de-identification dataset demonstrate strong performance, achieving macro-level F1 scores above 0.96 for several entity categories, while enabling quantitative prioritization of high-risk documents for further review. Our results highlight the effectiveness of combining neural de-identification with explicit risk modeling, supporting privacy-preserving data sharing in sensitive domains. Although evaluated on clinical text, the proposed framework is generalizable to other privacy-critical domains such as legal and administrative documents, where reliable pseudonymization and risk-aware anonymization are essential."
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<abstract>The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered framework for automated pseudonymization and re-identification risk assessment of clinical free-text data. Our approach integrates domain-adapted transformer models, including BioBERT and ClinicalBERT, into the MASK de-identification framework to improve the detection and masking of protected health information (PHI). Beyond entity recognition, we introduce a novel document-level risk assessment module that quantifies residual re-identification risk using a combination of k-anonymity, l-diversity, t-closeness, contextual similarity, and entity co-occurrence analysis. Experiments conducted on the i2b2 2014 de-identification dataset demonstrate strong performance, achieving macro-level F1 scores above 0.96 for several entity categories, while enabling quantitative prioritization of high-risk documents for further review. Our results highlight the effectiveness of combining neural de-identification with explicit risk modeling, supporting privacy-preserving data sharing in sensitive domains. Although evaluated on clinical text, the proposed framework is generalizable to other privacy-critical domains such as legal and administrative documents, where reliable pseudonymization and risk-aware anonymization are essential.</abstract>
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%0 Conference Proceedings
%T DeID-Clinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment
%A Paul, Angel
%A Shaji, Dhivin
%A Han, Lifeng
%A Del-Pinto, Warren
%A Nenadic, Goran
%A Verberne, Suzan
%Y Siegert, Ingo
%Y Szawerna, Maria Irena
%Y Choukri, Khalid
%Y Dobnik, Simon
%Y Kamocki, Paweł
%Y Lindström Tiedemann, Therese
%Y Lison, Pierre
%Y Muñoz Sánchez, Ricardo
%Y Pilán, Ildikó
%Y Södergård, Lisa
%Y Talmoudi, Kossay
%Y Volodina, Elena
%Y Vu, Xuan-Son
%S Proceedings of the Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy (LEGAL2026 and CALD-pseudo 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA
%C Palma, Mallorca (Spain)
%F paul-etal-2026-deid
%X The increasing availability of sensitive textual data has created an urgent need for robust de-identification methods that enable compliant data sharing while preserving downstream utility. This paper presents DeID-Clinic, a multi-layered framework for automated pseudonymization and re-identification risk assessment of clinical free-text data. Our approach integrates domain-adapted transformer models, including BioBERT and ClinicalBERT, into the MASK de-identification framework to improve the detection and masking of protected health information (PHI). Beyond entity recognition, we introduce a novel document-level risk assessment module that quantifies residual re-identification risk using a combination of k-anonymity, l-diversity, t-closeness, contextual similarity, and entity co-occurrence analysis. Experiments conducted on the i2b2 2014 de-identification dataset demonstrate strong performance, achieving macro-level F1 scores above 0.96 for several entity categories, while enabling quantitative prioritization of high-risk documents for further review. Our results highlight the effectiveness of combining neural de-identification with explicit risk modeling, supporting privacy-preserving data sharing in sensitive domains. Although evaluated on clinical text, the proposed framework is generalizable to other privacy-critical domains such as legal and administrative documents, where reliable pseudonymization and risk-aware anonymization are essential.
%R 10.63317/5muz36muepa2
%U https://aclanthology.org/2026.legal-1.5/
%U https://doi.org/10.63317/5muz36muepa2
%P 40-52
Markdown (Informal)
[DeID-Clinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment](https://aclanthology.org/2026.legal-1.5/) (Paul et al., LEGAL-CALD-pseudo 2026)
ACL
- Angel Paul, Dhivin Shaji, Lifeng Han, Warren Del-Pinto, Goran Nenadic, and Suzan Verberne. 2026. DeID-Clinic: A Risk-Aware Pseudonymization Framework for Clinical Text De-identification and Re-identification Risk Assessment. In Proceedings of the Joint Workshop on Legal and Ethical Issues in Human Language Technologies and Computational Approaches to Language Data Pseudonymization, Anonymization, De-identification, and Data Privacy (LEGAL2026 and CALD-pseudo 2026) @ LREC 2026, pages 40–52, Palma, Mallorca (Spain). ELRA.