Subject-level Inference for Realistic Text Anonymization Evaluation
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Computer Science > Computation and Language
Title:Subject-level Inference for Realistic Text Anonymization Evaluation
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.
| Comments: | Accepted at ACL 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2604.21211 [cs.CL] |
| (or arXiv:2604.21211v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.21211
arXiv-issued DOI via DataCite
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Submission history
From: Myeong Seok Oh [view email][v1] Thu, 23 Apr 2026 02:02:32 UTC (865 KB)
[v2] Thu, 25 Jun 2026 23:30:44 UTC (984 KB)
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