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ReCoPII: A Relation-Grounded Framework for Building Contextual PII Masking Datasets

Summary

Researchers propose ReCoPII, a framework that automatically builds contextual PII masking datasets by planning ontology-constrained entity relations and generating insertion sentences with placeholders. On Korean documents, it significantly outperforms SPY-style augmentation, and joint fine-tuning of a PII detector reduces over-masking within a fixed leakage budget.

SourcearXiv Computational LinguisticsAuthor: Taehwan Lee
ReCoPII: A Relation-Grounded Framework for Building Contextual PII Masking Datasets
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[Submitted on 14 Oct 2026]

Title:ReCoPII: A Relation-Grounded Framework for Building Contextual PII Masking Datasets

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Abstract:Training contextual masking systems for personally identifiable information (PII) requires examples distinguishing maskable from preservable mentions of one entity type, but privacy restrictions limit access to real data. Author-based augmentation (SPY) labels mentions unrelated to the author as non-PII, presuming a single data subject and overlooking others. We propose ReCoPII (Relation-grounded Contextual PII masking), a framework that builds such data without this premise: it plans ontology-constrained entity relations, writes insertion sentences with placeholders, reconstructs annotations with code, checks the result with a separate LLM, and derives MASK/KEEP labels from entity relations. On Korean documents from six domains, it yields ReCoPII-Ko, a corpus with relation, privacy, and MASK/KEEP annotations, which we validate. Across 300 pairs, four annotators agree with 88.42% of its sampled KEEP labels, against 26.27% for SPY-style augmentation of the same sources. Classifiers trained on it over-mask less than under that reference on a matched test. Contextual masking fine-tuned jointly with a PII detector removes most over-masking of detection alone within a fixed leakage budget; on human-verified entity records, the four detectors meeting it over-mask at most 5.04% with at most 4.18% leakage. The detector is comparable to XLM-R-large on KDPII with about a quarter of its parameters.

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

Cite as: arXiv:2610.35925 [cs.CL]

(or arXiv:2610.35925v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.35925

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Taehwan Lee [view email] [v1] Wed, 14 Oct 2026 09:48:32 UTC (6,601 KB)

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Key points

  • ReCoPII generates PII masking annotations without assuming a single data subject by planning entity relations, inserting placeholders, and verifying with an LLM.
  • Human annotators agree with 88.42% of its sampled KEEP labels across 300 pairs, versus 26.27% for SPY-style augmentation.
  • Joint fine-tuning of contextual masking with a PII detector cuts over-masking to at most 5.04% with at most 4.18% leakage.
  • A lightweight detector matches XLM-R-large on KDPII with about a quarter of its parameters.

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