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

A research team introduced ReCoPII, a relation-grounded framework that generates contextual PII masking datasets without relying on real personal data, producing the ReCoPII-Ko corpus from Korean documents across six domains. Across 300 pairs, four annotators agreed with 88.42% of ReCoPII's sampled KEEP labels, versus 26.27% for SPY-style augmentation of the same sources. Classifiers trained on the corpus over-mask less than the SPY reference on a matched test, and on human-verified entity records the four detectors meeting a fixed leakage budget over-mask at most 5.04% with at most 4.18% leakage, while the detector performs comparably to XLM-R-large on KDPII with about a quarter of its parameters.

arXiv:2610.35925v1 Announce Type: new 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.