Tau-Eval统一评估文本匿名化在隐私与效用间的平衡表现。
Tau-Eval: A Unified Evaluation Framework for Useful and Private Text Anonymization
- 从隐私与效用双重角度构建评估框架
- 支持多种匿名化方法的对比测试
- 适合研究隐私保护与自然语言处理的学者
文本匿名化旨在移除或混淆文本中的敏感信息以保护个人隐私。该过程本质上涉及隐私保护与信息保留之间的复杂权衡，过于严格的匿名化方法会显著影响文本在下游任务中的可用性。从隐私和效用两个角度评估文本匿名化效果极具挑战性，因为目前尚无通用基准能全面评估不同且有时相互矛盾情境下的匿名化技术。我们提出Tau-Eval，一个开源框架，通过隐私与效用任务敏感性视角来基准化文本匿名化方法。该框架提供Python库、代码、文档及教程，均可公开获取。
原文摘要 · Abstract (English)
Text anonymization is the process of removing or obfuscating information from textual data to protect the privacy of individuals. This process inherently involves a complex trade-off between privacy protection and information preservation, where stringent anonymization methods can significantly impact the text's utility for downstream applications. Evaluating the effectiveness of text anonymization proves challenging from both privacy and utility perspectives, as there is no universal benchmark that can comprehensively assess anonymization techniques across diverse, and sometimes contradictory contexts. We present Tau-Eval, an open-source framework for benchmarking text anonymization methods through the lens of privacy and utility task sensitivity. A Python library, code, documentation and tutorials are publicly available.
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