去标识的悖论：在大型语言模型时代对HIPAA安全港的批判
Paradox of De-identification: A Critique of HIPAA Safe Harbour in the Age of LLMs
AI总结：
本文批判HIPAA安全港在大型语言模型时代存在的不足，指出去标识的不完美性可能影响患者与提供者之间的信任，并提出通过因果图和实证研究揭示去标识悖论，呼吁社区采取行动。
摘要：
隐私是一项人权，它维持着患者与提供者之间的信任。临床笔记记录了患者隐私的脆弱性和个体性，用于协调护理和研究。根据HIPAA安全港规定，这些笔记被去标识以保护患者隐私。然而，安全港是为一个以分类表格数据为特征的时代设计的，其关注点是去除显式标识符，而忽略了在身份与准标识符之间的相关性中所发现的潜在信息，这些信息可以被现代LLMs捕获。我们首先使用因果图正式化这些相关性，然后通过重新识别被清除的笔记中的患者进行实证验证。通过诊断消融，进一步展示了去标识的悖论：即使在去除所有其他信息后，模型仍可以根据诊断本身预测患者的邻里情况。本文提出问题：当去标识本质上不完美时，我们如何作为社区维护患者与提供者之间的信任？我们旨在提高意识并讨论可行的建议。
英文摘要：
Privacy is a human right that sustains patient-provider trust. Clinical notes capture a patient's private vulnerability and individuality, which are used for care coordination and research. Under HIPAA Safe Harbor, these notes are de-identified to protect patient privacy. However, Safe Harbor was designed for an era of categorical tabular data, focusing on the removal of explicit identifiers while ignoring the latent information found in correlations between identity and quasi-identifiers, which can be captured by modern LLMs. We first formalize these correlations using a causal graph, then validate it empirically through individual re-identification of patients from scrubbed notes. The paradox of de-identification is further shown through a diagnosis ablation: even when all other information is removed, the model can predict the patient's neighborhood based on diagnosis alone. This position paper raises the question of how we can act as a community to uphold patient-provider trust when de-identification is inherently imperfect. We aim to raise awareness and discuss actionable recommendations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
