ArticleFrontiers in digital health2026
K-anonymity decay in multi-turn clinical large language model conversations.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
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Abstract
Per-prompt de-identification, a commonly adopted practice for protecting patient privacy in clinical artificial intelligence (AI) conversations, may be insufficient to bound re-identification risk when the same patient is discussed across multiple conversational turns. Under the Health Insurance Portability and Accountability Act (HIPAA) Safe Harbor method, eighteen specified identifier categories must be removed from each disclosure independently, but this approach does not assess cumulative re-identification risk: successive turns reveal additional quasi-identifiers that progressively narrow the set of matching records, eroding the privacy protection that any single turn appeared to provide. We quantified this vulnerability by simulating progressive quasi-identifier disclosure, modeled on clinical case presentations, against a synthetic electronic health record cohort. Under progressive disclosure, 79.9% of simulated patients fell below the small-cell threshold (
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