OpenAI Privacy Filter
OpenAI Privacy Filter is an open-weight, Apache 2.0-licensed token-classification model published by OpenAI on HuggingFace for detecting and masking personally identifiable information (PII) in text. It is designed for high-throughput data sanitization workflows: feeding LLMs, redacting datasets, on
Canonical version: OpenAI Privacy Filter.
OpenAI Privacy Filter is an open-weight, Apache 2.0-licensed token-classification model published by OpenAI on HuggingFace for detecting and masking personally identifiable information (PII) in text. It is designed for high-throughput data sanitization workflows: feeding LLMs, redacting datasets, on-prem privacy filtering. It is not a compliance guarantee — it is one layer in a broader privacy-by-design stack.
This is a notable release because OpenAI rarely ships open weights, and because privacy filtering is one of the highest-leverage things to add in front of any LLM pipeline that touches user data — particularly for anyone running BYOK or local inference setups (see Where Your AI Prompts Really Go - A Practical Guide to AI Privacy (Article)).
Specs
| Aspect | Detail |
|---|---|
| Developer | OpenAI |
| License | Apache 2.0 License (permissive) |
| Total params | 1.5B |
| Active params | 50M (sparse MoE, 128 experts, top-4) |
| Architecture | 8 transformer blocks, grouped-query attention, sparse MoE, single-pass |
| Context window | 128k tokens |
| Decoding | Constrained Viterbi over BIOES span labels |
| Distribution | HuggingFace (openai/privacy-filter), runs via Transformers or Transformers.js (browser) |
What it detects
Eight PII span categories:
- Account number
- Private address
- Private email
- Private person (name)
- Private phone
- Private URL
- Private date
- Secret
The model labels all tokens in a single pass (not autoregressive), then runs constrained Viterbi to produce coherent BIOES spans. A runtime threshold tunes the precision/recall tradeoff per use case.
Why it matters
- Open weights from OpenAI is rare. It also lowers the bar for self-hosted, on-prem privacy filtering.
- Right place in the stack. PII detection belongs in front of any third-party LLM call, especially for cloud AI APIs that retain inputs by default. See the four data paths in Where Your AI Prompts Really Go - A Practical Guide to AI Privacy (Article).
- Small, efficient. 50M active params + 128k context means it can sit in front of a request without dominating latency or cost.
- Fine-tunable. Domains with their own privacy taxonomies (medical, legal, HR) can extend the label set.
Limitations
- Not a compliance guarantee — should be one layer in a broader privacy-by-design approach.
- Performance drops on non-English text, non-Latin scripts, and underrepresented naming patterns.
- Failure modes include under-detection of uncommon names and over-redaction of public entities.
- High-sensitivity settings (medical, legal, financial, HR) still require human review.
Use cases
| Use case | How the filter fits |
|---|---|
| Pre-prompt sanitization | Strip PII before sending prompts to a third-party LLM API |
| Dataset cleaning | Redact PII from training, fine-tuning, or analytics datasets |
| On-prem privacy gateway | Filter inbound/outbound text in a self-hosted AI pipeline |
| Browser-side filtering | Run via Transformers.js to redact before data ever leaves the device |
| Custom redaction policies | Fine-tune for domain-specific PII (medical IDs, legal refs) |
References
Related
- OpenAI
- AI Privacy
- Where Your AI Prompts Really Go - A Practical Guide to AI Privacy (Article)
- HuggingFace
- Apache 2.0 License
- Transformers
- AI Mixture of Experts (MoE)
- Large Language Models (LLMs)
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