Abstract
Personally identifiable information (PII) anonymization is a high-stakes task that poses a barrier to many open-science data sharing initiatives. While PII identification has made large strides in recent years, in practice, error thresholds and the recall/precision trade-off still limit the uptake of these anonymization pipelines. We present PIIvot, a lighter-weight framework for PII anonymization that leverages knowledge of the data context to simplify the PII detection problem. To demonstrate its effectiveness, we also contribute QATD-2k, the largest open-source real-world tutoring dataset of its kind, to support the demand for quality educational dialogue data.
Abstract (translated)
个人可识别信息（PII）的匿名化是一项高风险任务，为许多开放科学数据共享倡议设置了障碍。虽然近年来在PII识别方面已经取得了显著进展，但在实践中，错误阈值和召回率/精确度权衡仍然限制了这些匿名化管道的应用推广。我们提出了一个名为PIIvot的轻量级框架，该框架利用对数据上下文的理解来简化PII检测问题。为了证明其有效性，我们也贡献了一个名为QATD-2k的数据集，这是同类中最大的开源现实世界辅导数据集，以支持高质量教育对话数据的需求。
URL
https://arxiv.org/abs/2505.16931
