用自动补全技术逆向还原被脱敏文档，验证脱敏安全性。
Re-identification of De-identified Documents with Autoregressive Infilling
- 基于检索+补全的两阶段反向还原方法
- 80%的敏感信息可被成功恢复
- 背景知识越丰富，还原准确率越高
包含个人敏感信息的文档通常需进行去标识化处理，即屏蔽所有个人身份信息（PII）以降低身份泄露风险。为评估去标识化方法的鲁棒性，我们提出一种受RAG启发的新方法，尝试从背景知识库中反向重构被遮蔽的文本内容。给定一段已去标识化的文本，该过程分为两步：首先由检索器筛选出与身份重建相关的背景知识片段；随后将这些片段输入补全模型，逐段推断原始内容。此过程重复进行，直至所有遮蔽部分被替换。我们在三个数据集（维基人物传记、法院判决书、临床记录）上进行评估，结果显示：（1）最多有80%的去标识化文本段落可被成功还原；（2）随着背景知识丰富度提升，重识别准确率也随之提高。
原文摘要 · Abstract (English)
Documents revealing sensitive information about individuals must typically be de-identified. This de-identification is often done by masking all mentions of personally identifiable information (PII), thereby making it more difficult to uncover the identity of the person(s) in question. To investigate the robustness of de-identification methods, we present a novel, RAG-inspired approach that attempts the reverse process of re-identification based on a database of documents representing background knowledge. Given a text in which personal identifiers have been masked, the re-identification proceeds in two steps. A retriever first selects from the background knowledge passages deemed relevant for the re-identification. Those passages are then provided to an infilling model which seeks to infer the original content of each text span. This process is repeated until all masked spans are replaced. We evaluate the re-identification on three datasets (Wikipedia biographies, court rulings and clinical notes). Results show that (1) as many as 80% of de-identified text spans can be successfully recovered and (2) the re-identification accuracy increases along with the level of background knowledge.
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