Paradox of De-identification: A Critique of HIPAA Safe Harbour in the Age of LLMs
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Abstract (arXiv)
Privacy is a human right that sustains patient-provider trust. Clinical notes capture a patient's private vulnerability and individuality, which are used for care coordination and research. Under HIPAA Safe Harbor, these notes are de-identified to protect patient privacy. However, Safe Harbor was designed for an era of categorical tabular data, focusing on the removal of explicit identifiers while ignoring the latent information found in correlations between identity and quasi-identifiers, which can be captured by modern LLMs. We first formalize these correlations using a causal graph, then validate it empirically through individual re-identification of patients from scrubbed notes. The paradox of de-identification is further shown through a diagnosis ablation: even when all other information is removed, the model can predict the patient's neighborhood based on diagnosis alone. This position paper raises the question of how we can act as a community to uphold patient-provider trust when de-identification is inherently imperfect. We aim to raise awareness and discuss actionable recommendations.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 9 Feb 2026.
Builds on
8 of 32 references.
References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2022Quantifying Memorization Across Neural Language Modelscited by 755
- 2024OLMo: Accelerating the Science of Language Modelscited by 599
- 2023Differentially Private Synthetic Data via Foundation Model APIs 1: Imagescited by 63
- 2019Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Recordscited by 11
- 2025Advancing Science- and Evidence-based AI Policycited by 7
- 2025Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Datacited by 4
- 2025Privacy-Preserving Dataset Combinationcited by 1
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Citing papers per year: 2026: 1.
1 paper.
- 2026Clinically Grounded Privacy Evaluation of Medical LMscited by 0
- 2026Clinically Grounded Privacy Evaluation of Medical LMs8 Jun 2026