没有入侵者，没有有效性：隐私保护文本分析的评价标准
No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization
摘要：
对于NLP研究人员和从业者之间共享的敏感文本数据，共享文档需要遵守数据保护和隐私法。因此，人们对文本匿名化的自动化方法越来越感兴趣。然而，衡量此类方法的性能具有挑战性：缺失一个识别属性就可能泄露个人的身份。在本文中，我们提请注意这个问题，并认为，研究人员和从业人员开发自动文本匿名化系统应仔细评估他们的评估方法是否真正反映了系统的能力，以保护个人被重新识别。然后，我们提出了TILD，一组评估标准，包括匿名化方法的技术性能，其匿名化造成的信息丢失，以及人类对编辑文档进行去匿名化的能力。这些标准可以促进朝着用于测量匿名化性能的标准化方式的进展。
英文摘要：
For sensitive text data to be shared among NLP researchers and practitioners, shared documents need to comply with data protection and privacy laws. There is hence a growing interest in automated approaches for text anonymization. However, measuring such methods' performance is challenging: missing a single identifying attribute can reveal an individual's identity. In this paper, we draw attention to this problem and argue that researchers and practitioners developing automated text anonymization systems should carefully assess whether their evaluation methods truly reflect the system's ability to protect individuals from being re-identified. We then propose TILD, a set of evaluation criteria that comprises an anonymization method's technical performance, the information loss resulting from its anonymization, and the human ability to de-anonymize redacted documents. These criteria may facilitate progress towards a standardized way for measuring anonymization performance.
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。
