Model Card for OpenAI Privacy Filter

Abstract:OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text. The model is derived from an autoregressively pretrained checkpoint and converted into a bidirectional, banded-attention classifier that labels an input sequence in a single forward pass. A constrained Viterbi decoder produces coherent spans across eight privacy categories and exposes configurable operating points for precision-recall tradeoffs. Privacy Filter has 1.5 billion total parameters, 50 million active parameters per token, and a 128,000-token context window. It is designed for efficient local deployment and domain-specific fine-tuning. Privacy Filter is intended as a configurable data-minimization component within layered privacy workflows, not as an anonymization or compliance guarantee.

Cryptography and Security (cs.CR); Machine Learning (cs.LG)

arXiv:2608.18274 [cs.CR]

(or arXiv:2608.18274v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2608.18274 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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