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arXiv · 2604.21211

Subject-level Inference for Realistic Text Anonymization Evaluation

Myeong Seok Oh·Dong-Yun Kim·Hanseok Oh·Chaean Kang·Joeun Kang·Xiaonan Wang·Hyunjung Park·Young Cheol Jung·Hansaem Kim
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Abstract

Current text anonymization evaluation relies on span-based metrics that fail to capture what an adversary could actually infer, and assumes a single data subject, ignoring multi-subject scenarios. To address these limitations, we present SPIA (Subject-level PII Inference Assessment), the first benchmark that shifts the unit of evaluation from text spans to individuals, comprising 675 documents across legal and online domains with novel subject-level protection metrics. Extensive experiments show that even when over 90% of PII spans are masked, subject-level inference protection drops as low as 33%, leaving the majority of personal information recoverable through contextual inference. Furthermore, target-subject-focused anonymization leaves non-target subjects substantially more exposed than the target subject. We show that subject-level inference-based evaluation is essential for ensuring safe text anonymization in real-world settings.

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Myeong Seok Oh, Dong-Yun Kim, Hanseok Oh, Chaean Kang, Joeun Kang, Xiaonan Wang, Hyunjung Park, Young Cheol Jung, Hansaem Kim. 2026-06-25. Subject-level Inference for Realistic Text Anonymization Evaluation. https://arxiv.org/abs/2604.21211

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