Evaluating OpenAI's Privacy Filter: Cross-Lingual, Cross-Domain PII Detection Across 42 Benchmarks
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Computer Science > Computation and Language
Title:Evaluating OpenAI's Privacy Filter: Cross-Lingual, Cross-Domain PII Detection Across 42 Benchmarks
Abstract:We present the first independent, systematic evaluation of OpenAI's Privacy Filter (OPF), a 1.5B-parameter bidirectional PII detector, across 42 synthetic benchmarks spanning 22 languages and 5 domains. Zero-shot, OPF achieves F1=0.855 on AI4Privacy and 0.464 on SPY medical, outperforming Presidio (0.431, 0.273) and XLM-RoBERTa (0.269, 0.111) on PII-annotated benchmarks; on multilingual NER, XLM-RoBERTa leads OPF on all 13 Indic and non-Latin languages. GPT-4o leads on medical, legal, and financial PII (SPY: 0.643 avg, Gretel: 0.527), while OPF leads on structured synthetic PII (0.71 avg) and customer support (0.60). OPF degrades sharply when PII is embedded in narrative prose: F1=0.04--0.57 on NER benchmarks and collapse for non-Latin scripts (Arabic: 0.04, Cyrillic: 0.03). Error analysis shows OPF is strongest on structurally regular PII types (email: 0.78, phone: 0.76) and weakest on culturally variable ones (person: 0.40, address: 0.49), and is recall-biased on customer-support and medical/legal PII (P=0.31--0.54, R=0.70--0.85); global precision spans 0.31--0.86 across all domains.
| Comments: | 11 pages, 5 tables; evaluation of a production PII detection system |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.02616 [cs.CL] |
| (or arXiv:2608.02616v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.02616
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