mlsanityData to 14 Oct 2026

Context-Aware Classification and Grading of Sensitive Information in Online Conversational Health Data

arXiv 2601.09717 ↗First posted 25 Dec 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Online medical consultations contain sensitive health information whose privacy implications depend not only on the entities mentioned but also on how those entities are described in context. Existing classification and grading approaches often map health-information entities directly to predefined sensitivity levels, potentially overlooking whether a condition is confirmed, suspected, negated, hypothetical, or merely planned for investigation. In this study, we formulate sensitive-information grading in online medical dialogues as a context-aware evaluation task. We develop a standard-informed operational framework that incorporates assertion status, experiencer, test-result status, and information granularity. We further design a naturalistic evaluation setting together with contrastive cases that minimally alter negation, uncertainty, experiencer, or granularity, and compare large language models under mention-only and full-context conditions. The study aims to quantify the contribution of contextual information to sensitivity grading and to characterize safety-critical over- and under-grading errors. Our framework provides a reproducible basis for evaluating whether LLMs can distinguish sensitive entity mentions from contextually established sensitive disclosures.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 25 Dec 2025.

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References · 9

  1. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 638
  2. 2023Beyond Memorization: Violating Privacy Via Inference with Large Language Modelscited by 175
  3. 2023DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4cited by 130
  4. 2024Robust Utility-Preserving Text Anonymization Based on Large Language Modelscited by 10
  5. 2024DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymizationcited by 4
  6. 2025Self-Refining Language Model Anonymizers via Adversarial Distillationcited by 4
  7. 2025From Learning to Unlearning: Biomedical Security Protection in Multimodal Large Language Modelscited by 3
  8. 2022Redactor: A Data-centric and Individualized Defense Against Inference Attackscited by 1
  9. 2024Healthcare Data Governance, Privacy, and Security -- A Conceptual Frameworkcited by 1

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  2. 2026EPPCMinerBen: A Novel Benchmark for Evaluating Large Language Models on Electronic Patient-Provider Communication via the Patient Portal
  3. 2026MedPriv-Bench: Benchmarking the Privacy-Utility Trade-off of Large Language Models in Medical Open-Ended Question Answering
  4. 2025Leveraging Open-Source Large Language Models for Clinical Information Extraction in Resource-Constrained Settings
  5. 2025How to Train Private Clinical Language Models: A Comparative Study of Privacy-Preserving Pipelines for ICD-9 Coding
  6. 2026JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese Large Language Models
  7. 2026Generating High Quality Synthetic Data for Dutch Medical Conversations
  8. 2023Does Synthetic Data Generation of LLMs Help Clinical Text Mining?