By name: openai-privacy-filter description: Methodology for building PII detection and redaction models using bidirectional token-classification with span decoding and constrained Viterbi. Based on OpenAI Privacy Filter (1.5B params, 50M active, 128K context). Use when implementing privacy-preserving NLP pipelines, PII detection systems, or data redaction workflows.
OpenAI Privacy Filter
Methodology for building privacy-preserving PII detection models inspired by OpenAI's Privacy Filter. Uses a bidirectional token-classification approach with span decoding.
Architecture Overview
The Privacy Filter architecture consists of:
- Base: Start from an autoregressive pretrained checkpoint
- Adaptation: Convert into a bidirectional token classifier over a fixed taxonomy of privacy labels
- Span Decoding: Use constrained Viterbi procedure for coherent span boundaries
- Output: BIOES span tags for clean masking boundaries
Key properties:
- Fast: All tokens labeled in a single forward pass
- Context-aware: Language prior enables detection based on surrounding context
- Long-context: Supports up to 128,000 tokens
- Configurable: Tunable operating points for precision vs. recall tradeoff
- Local execution: Can run on-device without data leaving the machine
Model Specs
- Total parameters: 1.5B
- Active parameters: 50M
- Context window: 128K tokens
- Architecture: Bidirectional token-classification with span decoding
PII Categories (8 types)
The model predicts spans across eight categories:
- PERSON (names)
- EMAIL (email addresses)
- PHONE (phone numbers)
- ADDRESS (physical addresses)
- ACCOUNT_NUMBER (credit cards, bank accounts, etc.)
- CREDENTIAL (passwords, API keys, etc.)
- DATE (dates, times)
- URL (web addresses)
Labels decoded with BIOES span tags.
Training Approach
- Fine-tune from autoregressive pretrained checkpoint
- Adapt into token classifier over privacy label taxonomy
- Use constrained Viterbi procedure for coherent span decoding
- Achieves SOTA on PII-Masking-300k benchmark
Usage Patterns
Deployment Options
- Run locally for privacy: PII can be masked without leaving the machine
- High-throughput: Single pass processing
- Production-grade: Configurable precision/recall tradeoff
Integration
- Training pipelines (data sanitization)
- Indexing pipelines (content filtering)
- Logging pipelines (log redaction)
- Review pipelines (content moderation)
Related Resources
- OpenAI Privacy Filter Announcement
- PII-Masking-300k benchmark
Activation
- Keywords: PII detection, privacy filter, data redaction, token classification, span decoding, Viterbi decoding, personal information, data sanitization