针对医疗数据隐私风险，提出分层双策略遗忘机制，精准删除敏感知识。
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
- 分层双策略：几何约束梯度更新+概念感知令牌干预
- 遗忘率82.7%，知识保留率达88.5%，仅修改0.1%参数
- 适合医疗AI伦理合规、隐私保护与可审计场景
大语言模型在医疗领域表现优异，但存在训练数据记忆带来的隐私风险，尤其在包含不完整或敏感患者信息的场景下。本文提出一种分层双策略选择性知识遗忘框架，能精准移除特定医学知识，同时保留基础医学能力。该方法融合几何约束梯度更新以选择性调节目标参数，并通过统一四层医学概念体系实现令牌级别的概念感知干预，区分需保留与需遗忘的语义单元。在MedMCQA（外科）和MHQA（焦虑、抑郁、创伤）数据集上的综合评估显示，该框架达到82.7%的遗忘率与88.5%的知识保留率。值得注意的是，该框架在仅修改0.1%参数的前提下仍保持强隐私保障，满足临床研究中的监管合规、可审计性与伦理要求。
原文摘要 · Abstract (English)
Large language models (LLMs) exhibit exceptional performance but pose substantial privacy risks due to training data memorization, particularly within healthcare contexts involving imperfect or privacy-sensitive patient information. We present a hierarchical dual-strategy framework for selective knowledge unlearning that precisely removes specialized knowledge while preserving fundamental medical competencies. Our approach synergistically integrates geometric-constrained gradient updates to selectively modulate target parameters with concept-aware token-level interventions that distinguish between preservation-critical and unlearning-targeted tokens via a unified four-level medical concept hierarchy. Comprehensive evaluations on the MedMCQA (surgical) and MHQA (anxiety, depression, trauma) datasets demonstrate superior performance, achieving an 82.7% forgetting rate and 88.5% knowledge preservation. Notably, our framework maintains robust privacy guarantees while requiring modification of only 0.1% of parameters, addressing critical needs for regulatory compliance, auditability, and ethical standards in clinical research.
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