提升教育数据中个人可识别信息的脱敏
Enhancing the De-identification of Personally Identifiable Information in Educational Data
AI总结：
本文提出使用微调后的GPT-4o-mini模型高效检测教育数据中的PII，实现高召回率和低计算成本，为隐私保护提供新的解决方案。
摘要：
保护个人可识别信息（PII），如姓名，在学习技术中是至关重要的要求，以保护学生和教师的隐私并维护信任。准确的PII检测是匿名化敏感信息的关键步骤，同时保持教育数据的效用。受最近人工智能进展的启发，我们的研究探讨了GPT-4o-mini模型作为成本效益高且高效的PII检测解决方案。我们探索了提示和微调方法，并将GPT-4o-mini的表现与Microsoft Presidio和Azure AI Language等现有框架进行比较。我们在两个公开数据集CRAPII和TSCC上的评估表明，微调后的GPT-4o-mini模型在CRAPII上实现了95.89%的召回率。此外，微调后的GPT-4o-mini显著提高了精确度分数（增加三倍），同时将计算成本降低到Azure AI Language的几乎十分之一。此外，我们的偏见分析显示，微调后的GPT-4o-mini模型在不同文化背景和性别中均能提供准确的结果。使用TSCC数据集进行的泛化分析进一步突显了其鲁棒性，仅需少量额外训练数据即可达到98.95%的召回率。这些结果强调了微调后的GPT-4o-mini在教育数据PII检测中的潜力。它提供了一种准确且成本效益高的工具，能够在保护隐私的同时保持数据对研究和教学分析的效用。我们的代码可在GitHub上获取：https://github.com/AnonJD/PrivacyAI
英文摘要：
Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in learning technologies to safeguard student and teacher privacy and maintain trust. Accurate PII detection is an essential step toward anonymizing sensitive information while preserving the utility of educational data. Motivated by recent advancements in artificial intelligence, our study investigates the GPT-4o-mini model as a cost-effective and efficient solution for PII detection tasks. We explore both prompting and fine-tuning approaches and compare GPT-4o-mini's performance against established frameworks, including Microsoft Presidio and Azure AI Language. Our evaluation on two public datasets, CRAPII and TSCC, demonstrates that the fine-tuned GPT-4o-mini model achieves superior performance, with a recall of 0.9589 on CRAPII. Additionally, fine-tuned GPT-4o-mini significantly improves precision scores (a threefold increase) while reducing computational costs to nearly one-tenth of those associated with Azure AI Language. Furthermore, our bias analysis reveals that the fine-tuned GPT-4o-mini model consistently delivers accurate results across diverse cultural backgrounds and genders. The generalizability analysis using the TSCC dataset further highlights its robustness, achieving a recall of 0.9895 with minimal additional training data from TSCC. These results emphasize the potential of fine-tuned GPT-4o-mini as an accurate and cost-effective tool for PII detection in educational data. It offers robust privacy protection while preserving the data's utility for research and pedagogical analysis. Our code is available on GitHub: https://github.com/AnonJD/PrivacyAI
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。
