MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-Ended Question Answering
Recent advances in Retrieval-Augmented Generation enable LLMs to ground outputs in clinical evidence, but connections to external databases create the risk of contextual leakage, where unique combinations of medical details enable patient re-identification without explicit identifiers. Existing healthcare benchmarks emphasize accuracy while overlooking this risk. To fill this gap, we present MedPriv-Bench, the first benchmark for jointly evaluating privacy preservation and clinical utility in medical open-ended question answering. Our framework utilizes a multi-agent, human-in-the-loop pipeline to synthesize sensitive medical contexts and clinically relevant queries that create realistic ...
| Authors | Shaowei Guan, Yu Zhai, Hin Chi Kwok et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 March 2026 |
| Last updated at source | 25 August 2026 |
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