@inproceedings{oh-etal-2026-subject,
title = "Subject-level Inference for Realistic Text Anonymization Evaluation",
author = "Oh, Myeong Seok and
Kim, Dong-Yun and
Oh, Hanseok and
Kang, Chaean and
Kang, Joeun and
Wang, Xiaonan and
Park, Hyunjung and
Jung, Young Cheol and
Kim, Hansaem",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.778/",
doi = "10.18653/v1/2026.acl-long.778",
pages = "17100--17135",
ISBN = "979-8-89176-390-6",
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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<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.</abstract>
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%0 Conference Proceedings
%T Subject-level Inference for Realistic Text Anonymization Evaluation
%A Oh, Myeong Seok
%A Kim, Dong-Yun
%A Oh, Hanseok
%A Kang, Chaean
%A Kang, Joeun
%A Wang, Xiaonan
%A Park, Hyunjung
%A Jung, Young Cheol
%A Kim, Hansaem
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F oh-etal-2026-subject
%X 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.
%R 10.18653/v1/2026.acl-long.778
%U https://aclanthology.org/2026.acl-long.778/
%U https://doi.org/10.18653/v1/2026.acl-long.778
%P 17100-17135
Markdown (Informal)
[Subject-level Inference for Realistic Text Anonymization Evaluation](https://aclanthology.org/2026.acl-long.778/) (Oh et al., ACL 2026)
ACL
- Myeong Seok Oh, Dong-Yun Kim, Hanseok Oh, Chaean Kang, Joeun Kang, Xiaonan Wang, Hyunjung Park, Young Cheol Jung, and Hansaem Kim. 2026. Subject-level Inference for Realistic Text Anonymization Evaluation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 17100–17135, San Diego, California, United States. Association for Computational Linguistics.