# PII Masker — Redact CPF, CNPJ, Emails, Phones, Names (LGPD) (`adorable_partial/pii-masker`) Actor

Find and mask personal data in texts, JSON or any Apify dataset: CPF, CNPJ (incl. alphanumeric), e-mails, phones, cards, IBAN, IPs and names. Redact, mask, partial or hash. Check-digit validated. LGPD/GDPR ready.

- **URL**: https://apify.com/adorable_partial/pii-masker.md
- **Developed by:** [Leandro Zanatta](https://apify.com/adorable_partial) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.35 / 1,000 record processeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## PII Masker — Redact CPF, CNPJ, E-mails, Phones & Names (LGPD / GDPR)

Find and hide **personal data** in **texts**, **JSON records** or **any Apify dataset** in one call. Detects Brazilian documents (**CPF**, **CNPJ including the new alphanumeric format**, **PIS**, **RG**, **CEP**, **license plates**) and international data (**e-mails**, **phone numbers**, **credit cards**, **IBAN**, **IP addresses**), plus **person names** with a small language model. Choose how to hide them: **redact**, **mask**, **partial** or **hash** (consistent pseudonyms).

![One fictitious record shown original, redacted, masked, partially masked and hashed](https://api.apify.com/v2/key-value-stores/GgLeCf7RGgtQJIc8g/records/demo.png?signature=ArTh8olckQN6ifn0jDV3)

*Real output of this Actor on fictitious data.*

### Why use it

- ✅ **Fewer false alarms**: CPF, CNPJ and PIS are validated by their **check digits**, cards by the **Luhn** algorithm and IBANs by **mod-97**. Order numbers, prices, dates, barcodes and protocol numbers are left alone.
- 🇧🇷 **Built for Brazil (LGPD)**: CPF, CNPJ (numeric and the **alphanumeric CNPJ** in use since July 2026), PIS/PASEP/NIS, RG, CEP and Mercosur plates, in all their usual formats.
- 🧩 **Keeps your data usable**: JSON structure stays the same; only the sensitive values change. **Hash** turns each value into the same token every time, so you can still count, group and join records.
- 🔗 **Plugs into any scraper**: give it a **dataset ID** and it cleans the whole output of another Apify run, ready to share or load into BI and AI tools.
- 🤖 **Safer AI**: strip personal data before sending tickets, chats or documents to an LLM, a vector database or a third-party API.
- 🔒 **Runs inside the Actor**: no external AI or API is called; nothing is logged.

### Who uses it

| Sector | Typical use |
|---|---|
| **Companies under LGPD / GDPR** | Anonymize exports before sharing with partners, vendors or analytics teams |
| **Customer support & CX** | Clean tickets, chats and call transcripts before analysis or AI training |
| **Data & AI teams** | Remove PII from datasets before LLM fine-tuning, RAG or vector search |
| **Developers & QA** | Turn production data into safe test data, keeping formats and joins |
| **Scraping & lead workflows** | Mask personal fields in scraped data before storage or resale of aggregated insights |
| **Legal, health & finance** | Prepare documents and records for audits, research and data-subject requests |

### What it detects

| Type | Examples | Validation |
|---|---|---|
| **CPF** | `529.982.247-25`, `52998224725` | Check digits; unformatted numbers only with "CPF" or a matching field name nearby |
| **CNPJ** | `11.222.333/0001-81`, `12.ABC.345/01DE-35` | Check digits, numeric and alphanumeric |
| **PIS / PASEP / NIS** | `120.12345.67-2` | Check digit |
| **RG** | `RG: 12.345.678-9` | Needs an "RG / identidade" label (no national check digit) |
| **E-mail** | `maria@empresa.com.br` | Format |
| **Phone** | `(11) 98765-4321`, `+55 21 99876-5432`, `0800 123 4567`, `+1 415 555 2671` | Format; not part of longer codes |
| **Credit card** | `4111 1111 1111 1111`, `3782 822463 10005` | Luhn + card formats; barcodes and order numbers ignored |
| **IBAN** | `GB82 WEST 1234 5698 7654 32` | mod-97 |
| **IP address** | `192.168.0.12`, IPv6 | Valid ranges; "version 10.2.3.4" ignored |
| **CEP** | `01310-100` | Format with hyphen |
| **License plate (BR)** | `ABC1D23`, `ABC-1234` | Old and Mercosur formats |
| **Person name** | `Maria da Silva`, `John Smith` | Language model (Portuguese and English) |

**Measured on 500 synthetic texts** mixing real-format documents with order numbers, prices, dates, barcodes and protocol codes: **100% of CPF, CNPJ, card, phone, e-mail, CEP, IP and plate values found, 0 false positives**. Person names: **96% found** in 200 simple Portuguese and English sentences, with no city, street or company names flagged. Real texts are messier than test data; review a sample for critical uses.

### Masking methods

| Method | `CPF 529.982.247-25` becomes | Best for |
|---|---|---|
| **Redact** (default) | `CPF [CPF]` | Sharing, AI prompts, logs |
| **Mask** | `CPF ***.***.***-**` | Keeping the layout of documents and screens |
| **Partial** | `CPF ***.982.247-**` | Customer service screens (the LGPD-style CPF display), e-mail `m***@gmail.com`, card `**** 1111` |
| **Hash** | `CPF CPF_5d91b935fb0f` | Analytics and joins: the same value gives the same token (with the same secret) |

For **hash**, set a **Hash secret** and reuse it in every run to get the same tokens across runs. Without a secret, a random one is used, so tokens only match within the run. Hashing with a secret is **pseudonymization**: under the LGPD and GDPR, whoever holds the secret can still link records, so keep it safe.

### Input

| Field | Type | Default | Description |
|---|---|---|---|
| **Texts** (`texts`) | array | — | Free texts; each is one record |
| **JSON records** (`records`) | array | — | Objects of any shape; structure is kept |
| **Apify dataset ID** (`datasetId`) | string | — | Clean every item of an existing dataset (e.g. a scraper's output) |
| **Masking method** (`method`) | string | `redact` | `redact`, `mask`, `partial` or `hash` |
| **Data types** (`piiTypes`) | array | all documents | Which types to look for |
| **Detect person names** (`detectNames`) | boolean | `true` | Use the language model for names |
| **Language for names** (`namesLanguage`) | string | `auto` | `auto`, `pt` or `en` |
| **Only these fields** (`fields`) | array | all | Dot paths such as `contact.email` |
| **Hash secret** (`hashSecret`) | string | random | Secret for consistent hashing (stored encrypted) |
| **Return positions** (`includeEntities`) | boolean | `false` | Add type, field and character positions of each finding (never the original value) |
| **Add \_pii summary** (`addSummary`) | boolean | `true` | Count of findings per record |
| **Max records** (`maxRecords`) | integer | `0` | Stop after N records (0 = all) |

Example: clean the output of a scraper run, keeping joins possible:

```json
{
  "datasetId": "aBcDeFgHiJkLmNoP",
  "method": "hash",
  "hashSecret": "my-long-random-secret",
  "fields": ["name", "email", "phone", "comments"]
}
```

### Output

One dataset row per text or record, in the same order. JSON records keep their structure:

```json
{
  "name": "A. S.",
  "cpf": "***.982.247-**",
  "contact": { "email": "a***@empresa.com.br", "phone": "+** ** *****-5432" },
  "note": "Entrega no CEP *****-*00",
  "_pii": { "found": 5, "byType": { "person": 1, "cpf": 1, "email": 1, "phone": 1, "cep": 1 } }
}
```

Texts come back as `{ "index": 0, "text": "Cliente [PERSON], CPF [CPF], ...", "_pii": {...} }`. A run summary (records processed and findings per type) is saved as **`SUMMARY`** in the key-value store. Original values are **never** written to the output.

### Pricing

Pay per **record processed** (a text or a JSON record of up to 5,000 characters; longer ones count once per started 5,000 characters), plus a tiny per-run start fee. No subscription. Apify plan discounts apply automatically (see the **Pricing** tab).

| Example | Records billed |
|---|---|
| 1,000 support tickets | 1,000 |
| A scraper dataset with 20,000 items | 20,000 |
| One 12,000-character document | 3 |

Use **Max records** or the run's *maximum cost* setting to cap spending.

### Use it from code or an AI agent

**Python: clean texts before sending them to an LLM**

```python
from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("adorable_partial/pii-masker").call(run_input={
    "texts": ["Oi, sou a Maria Souza, CPF 529.982.247-25, meu e-mail é maria@gmail.com"],
    "method": "redact",
})
for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row["text"])  # Oi, sou a [PERSON], CPF [CPF], meu e-mail é [EMAIL]
```

**JavaScript: mask a scraper's output**

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' });
const scrape = await client.actor('<some-scraper>').call({ /* ... */ });
const run = await client.actor('adorable_partial/pii-masker').call({
    datasetId: scrape.defaultDatasetId,
    method: 'partial',
});
const { items } = await client.dataset(run.defaultDatasetId).listItems();
```

**HTTP**

```bash
curl -X POST "https://api.apify.com/v2/acts/adorable_partial~pii-masker/run-sync-get-dataset-items?token=<YOUR_APIFY_TOKEN>" \
  -H "Content-Type: application/json" \
  -d '{"texts": ["Contato: joao@empresa.com, (11) 98765-4321"]}'
```

**No-code and agents:** chain it after any Actor with an **integration** or **webhook**, use it in **Make**, **Zapier** or **n8n**, or call it from an AI agent through the **Apify MCP server** as a privacy step.

### FAQ

**Does it keep the original values anywhere?** No. Only masked values are written. With `includeEntities` you get positions and types, never the values.

**Is masked data anonymous under the LGPD / GDPR?** *Redact* and *mask* remove the values; whether the remaining text is anonymous depends on what else it contains (e.g. a rare job title plus a city). *Partial* and *hash* are pseudonymization, still personal data under the law. Review your use case with your DPO.

**Why was a number not detected as CPF?** Unformatted 11-digit numbers pass the CPF check digit by chance about 1 time in 10, so they are only treated as CPF when "CPF" (or a field name like `cpf_cliente`) is nearby. Formatted CPFs (`529.982.247-25`) are always detected.

**Which languages are supported for names?** Portuguese and English models. Documents, e-mails, phones and cards work in any language.

**Can it process PDFs or images?** Not directly; extract the text first. For faces and plates in images and videos, see the anonymizer below.

**Models and licenses:** spaCy `pt_core_news_sm` (CC BY-SA 4.0) and `en_core_web_sm` (MIT) for names; everything else is rule-based.

### Related Actors

- [Image & Video Anonymizer](https://apify.com/adorable_partial/image-anonymizer) — blur faces and license plates in photos and videos
- [Audio & Video to Text (Whisper)](https://apify.com/adorable_partial/whisper-audio-video-transcriber) — transcribe calls, then mask the transcripts here
- [Face Compare](https://apify.com/adorable_partial/face-compare) — face verification and embeddings
- [Video Frame Extractor for AI](https://apify.com/adorable_partial/video-frame-extractor-ai) — turn videos into clean image datasets

### Support

Missing a document type or format? Open an issue in the **Issues** tab with a (fictitious) example.

### Need a different tool? Request it

Missing a feature, an input or output format, or a whole different tool for your workflow? **Open an issue in the Issues tab** and describe what you need:

- what goes in (files, URLs, data) and what should come out,
- roughly how much you process (per day or month),
- one example, if you can share it.

Requests with a real use case get built, either as a new option in this Actor or as a new Actor in [this collection](https://apify.com/adorable_partial), and you get notified in the issue when it's live. Every request is read.

# Actor input Schema

## `texts` (type: `array`):

Free texts to clean (messages, tickets, reviews, transcripts...). Each text is one record.

## `records` (type: `array`):

JSON objects to clean. Structure is kept; only values with personal data change.

## `datasetId` (type: `string`):

Clean every item of an existing Apify dataset, e.g. the output of a scraper run (pick it from your storage or paste its ID). Items are processed like JSON records.

## `method` (type: `string`):

How found values are replaced. Hash keeps joins possible: the same value always becomes the same token (with the same secret).

## `piiTypes` (type: `array`):

Types to detect. Empty = all except person names (use the switch below for names).

## `detectNames` (type: `boolean`):

Find person names with a small language model (Portuguese and English). Slower, and names are harder than documents: some may be missed or over-detected.

## `namesLanguage` (type: `string`):

Language model used for names.

## `fields` (type: `array`):

For JSON records and datasets: only process these keys (dot paths like contact.email). Empty = all fields.

## `hashSecret` (type: `string`):

Secret key for the hash method. Use the same secret in every run to get the same tokens. Without it, a random secret is used per run.

## `includeEntities` (type: `boolean`):

Add \_piiEntities with type, field and character positions (never the original value).

## `addSummary` (type: `boolean`):

Add a \_pii field with the number of findings per type.

## `maxRecords` (type: `integer`):

Stop after N records (0 = no limit).

## Actor input object example

```json
{
  "texts": [
    "Cliente Maria da Silva, CPF 529.982.247-25, e-mail maria.silva@gmail.com, tel (11) 98765-4321. Empresa CNPJ 11.222.333/0001-81.",
    "Order from John Smith (john.smith@acme.com, +1 415 555 2671), card 4111 1111 1111 1111, IP 192.168.0.12."
  ],
  "records": [
    {
      "name": "Ana Souza",
      "cpf": "52998224725",
      "contact": {
        "email": "ana@empresa.com.br",
        "phone": "+55 21 99876-5432"
      },
      "note": "Entrega no CEP 01310-100"
    }
  ],
  "method": "redact",
  "piiTypes": [
    "email",
    "cnpj",
    "cpf",
    "pis",
    "credit_card",
    "iban",
    "rg",
    "ip_address",
    "license_plate",
    "cep",
    "phone"
  ],
  "detectNames": true,
  "namesLanguage": "auto",
  "includeEntities": false,
  "addSummary": true,
  "maxRecords": 0
}
```

# Actor output Schema

## `records` (type: `string`):

Your texts or records with personal data masked, plus a \_pii summary per record.

## `summary` (type: `string`):

Records processed and findings per type.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "texts": [
        "Cliente Maria da Silva, CPF 529.982.247-25, e-mail maria.silva@gmail.com, tel (11) 98765-4321. Empresa CNPJ 11.222.333/0001-81.",
        "Order from John Smith (john.smith@acme.com, +1 415 555 2671), card 4111 1111 1111 1111, IP 192.168.0.12."
    ],
    "records": [
        {
            "name": "Ana Souza",
            "cpf": "52998224725",
            "contact": {
                "email": "ana@empresa.com.br",
                "phone": "+55 21 99876-5432"
            },
            "note": "Entrega no CEP 01310-100"
        }
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("adorable_partial/pii-masker").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "texts": [
        "Cliente Maria da Silva, CPF 529.982.247-25, e-mail maria.silva@gmail.com, tel (11) 98765-4321. Empresa CNPJ 11.222.333/0001-81.",
        "Order from John Smith (john.smith@acme.com, +1 415 555 2671), card 4111 1111 1111 1111, IP 192.168.0.12.",
    ],
    "records": [{
            "name": "Ana Souza",
            "cpf": "52998224725",
            "contact": {
                "email": "ana@empresa.com.br",
                "phone": "+55 21 99876-5432",
            },
            "note": "Entrega no CEP 01310-100",
        }],
}

# Run the Actor and wait for it to finish
run = client.actor("adorable_partial/pii-masker").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "texts": [
    "Cliente Maria da Silva, CPF 529.982.247-25, e-mail maria.silva@gmail.com, tel (11) 98765-4321. Empresa CNPJ 11.222.333/0001-81.",
    "Order from John Smith (john.smith@acme.com, +1 415 555 2671), card 4111 1111 1111 1111, IP 192.168.0.12."
  ],
  "records": [
    {
      "name": "Ana Souza",
      "cpf": "52998224725",
      "contact": {
        "email": "ana@empresa.com.br",
        "phone": "+55 21 99876-5432"
      },
      "note": "Entrega no CEP 01310-100"
    }
  ]
}' |
apify call adorable_partial/pii-masker --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,adorable_partial/pii-masker"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Hn3G1bCRLOF60AucO/builds/18uFaUrIgqDlX8oCX/openapi.json