Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches
Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) have shown strong performance on clinical deidentification, the task of identifying sensitive identifiers to protect privacy.However, previous work has not examined their generalizability between formats, cultures, and genders.In this work, we systematically evaluate fine-tuned transformer models (BERT, ClinicalBERT, ModernBERT), small LLMs (Llama 1-8B, Qwen 1.5-7B), and large LLMs (Llama-70B, Qwen-72B) at de-identification.We show that smaller models achieve comparable performance while substantially reducing inference cost, making them more practical for deployment.Moreover, we demonstrate that smaller models can be fine-tuned with limited data to outperform larger models in de-identifying identifiers drawn from Mandarin, Hindi, Spanish, French, Bengali, and regional variations of English, in addition to gendered names.To improve robustness in multi-cultural contexts, we introduce and publicly release BERT-MultiCulture-DEID, a set of de-identification models based on BERT, ClinicalBERT, and ModernBERT, fine-tuned on MIMIC with identifiers from multiple language variants.Our findings provide the first comprehensive quantification of the efficiency-generalizability trade-off in deidentification and establish practical pathways for fair and efficient clinical de-identification.
Institution: University of Alberta
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140107140