REDACT: A Systematically Controlled Multilingual Benchmark for Personal Information Detection
arXiv:2606.19881v1 Announce Type: new Abstract: Benchmark infrastructure for personally identifiable information (PII) detection remains limited: existing corpora cover few entity types, use ad hoc generation conditions, and do not show which surface conditions cause detector failures. We present REDACT, a systematically controlled multilingual PII benchmark with 13,427 records, 324,078 entity annotations, 51 entity types, 4,127 surface-form patterns, and 25 languages across 9 scripts. A strength-2 covering-array sampler controls nine generation axes: domain, format, difficulty, length, densit
The proliferation of powerful AI models and increasing regulatory scrutiny on data privacy necessitate more robust and comprehensive benchmarks for personal information detection.
A systematically controlled, multilingual PII benchmark like REDACT is crucial for developing and evaluating AI systems that handle sensitive personal data responsibly and compliantly across diverse linguistic and cultural contexts.
The availability of REDACT will enable more rigorous testing and comparison of PII detection models, leading to improved privacy-preserving AI and potentially influencing future data governance standards.
- · AI developers focused on privacy
- · Data privacy regulators
- · Multinational corporations handling personal data
- · Organizations with inadequate PII detection systems
- · Ad-hoc PII benchmark creators
Improved performance and reliability of PII detection AI models.
Increased trust in AI applications that process personal information, potentially accelerating their adoption in sensitive sectors.
Enhanced global data privacy compliance, reducing cross-border data transfer friction and harmonizing data protection practices.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL