Subject-level Inference for Realistic Text Anonymization Evaluation
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. Furt...
| Authors | Myeong Seok Oh, Dong-Yun Kim, Hanseok Oh et al. |
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| Primary category | cs.CL |
| Published | 23 April 2026 |
| Last updated at source | 25 June 2026 |
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