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For sensitive text data to be shared among NLP researchers and practitioners,shared documents need to comply with data protection and privacy laws. There ishence a growing interest in automated approaches for text anonymization.However, measuring such methods' performance is challenging: missing a singleidentifying attribute can reveal an individual's identity. In this paper, wedraw attention to this problem and argue that researchers and practitionersdeveloping automated text anonymization systems should carefully assess whethertheir evaluation methods truly reflect the system's ability to protectindividuals from being re-identified. We then propose TILD, a set of evaluationcriteria that comprises an anonymization method's technical performance, theinformation loss resulting from its anonymization, and the human ability tode-anonymize redacted documents. These criteria may facilitate progress towardsa standardized way for measuring anonymization performance.

No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization