MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-Ended Question Answering

[2603.14265] MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-Ended Question Answering Skip to main content Search Submit Donate Log in Computer Science > Computation and Language arXiv:2603.14265 (cs) [Submitted on 15 Mar 2026 ( v1 ), last revised 25 Aug 2026 (this version, v2)] Title: MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-Ended Question Answering Authors: Shaowei Guan , Yu Zhai , Hin Chi Kwok , Jiawei Du , Xinyu Feng , Jing Li , Harry Qin , Vivian Hui View PDF HTML (experimental) Abstract: 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 privacy pressure. We also establish an automated evaluation protocol using a fine-tuned RoBERTa-NLI model, which achieved an instance-level F1 score of 75.3%, sensitivity of 90.7%, and an average inference time of 0.056 s per sample against human annotations. Across nine LLMs and three privacy-preserving methods, we observed a pervasive privacy-utility trade-off. Relative to unprotected Med42-v2-8B (utility 3.87/5; leakage 72.8%), supervised fine-tuning improved utility to 4.25 and reduced leakage to 38.9%, whereas local differential privacy reduced leakage to 20.5% but lowered utility to 3.03. These results demonstrate the need for domain-specific benchmarks to validate medical AI systems in privacy-sensitive settings. Comments: Accepted to EMNLP 2026 Main Conference Subjects: Computation and Language (cs.CL) ; Multiagent Systems (cs.MA) Cite as: arXiv:2603.14265 [cs.CL] (or arXiv:2603.14265v2 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2603.14265 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.18653/2026.emnlp-main.1157 Focus to learn more DOI(s) linking to related resources Submission history From: Shaowei Guan [ view email ] [v1] Sun, 15 Mar 2026 07:47:35 UTC (5,070 KB) [v2] Tue, 25 Aug 2026 07:17:10 UTC (1,297 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.CL < prev | next > new | recent | 2026-03 Change to browse by: cs cs.MA References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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