PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues

• URL: http://arxiv.org/abs/2505.16931v1
• Date: Thu, 22 May 2025 17:22:28 GMT
• Title: PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues
• Authors: Matthew Zent, Digory Smith, Simon Woodhead,
• Abstract summary: PIIvot is a framework for PII anonymization that leverages knowledge of the data context to simplify the PII detection problem.<n>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.
• Score: 5.264430938065097
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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