Privacy Filter

Private TEE

openai/privacy-filter

PII detection (token classification) — returns spans for emails, phones, addresses, names, account numbers, secrets. NEAR AI runs this model in a TEE; prompts are not anonymized by the model itself, the cloud-api wraps it to do redaction.

Input / 1M tokens: $0.01
Output / 1M tokens: $0
Cache read / 1M tokens: —
Context window: 128K tokens

Privacy & execution

This model supports private inference inside a hardware-isolated Trusted Execution Environment (TEE), protecting data while it is processed. HTTPS protects data in transit; client-to-model end-to-end encryption depends on the model and integration.

Attestation provides cryptographic evidence of the execution environment’s identity and configuration. Where response signatures are available, you can also verify the integrity of the request and response.

Verification documentation

Make your first request

Call /v1/privacy/classify over HTTPS.

Detect personal information and return its spans, categories, and confidence scores.

Get an API key and replace the API key placeholder below.

curl --fail-with-body https://cloud-api.near.ai/v1/privacy/classify \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <YOUR-NEAR-AI-CLOUD-API-KEY>" \
  -d '{
  "model": "openai/privacy-filter",
  "input": "My name is John Smith and my email is john@example.com"
}'

Use the API

Detect personal information and return its spans, categories, and confidence scores.

Example

const response = await fetch("https://cloud-api.near.ai/v1/privacy/classify", {
  method: "POST",
  headers: {
    "Authorization": "Bearer <YOUR-NEAR-AI-CLOUD-API-KEY>",
    "Content-Type": "application/json"
  },
  body: JSON.stringify({
    "model": "openai/privacy-filter",
    "input": "My name is John Smith and my email is john@example.com"
  })
});
if (!response.ok) throw new Error(await response.text());
console.log(await response.json());