# PII Redactor - Detect & Mask Emails, Phones, SSNs, Cards (`nibble/pii-redactor`) Actor Redact or pseudonymously mask PII (emails, phones, SSNs, credit cards, IPs, names, addresses) in user-supplied text, CSV, or JSON. Outputs cleaned data plus a redaction log. - **URL**: https://apify.com/nibble/pii-redactor.md - **Developed by:** [Simon Fletcher](https://apify.com/nibble) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 record scanneds This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README PII Redactor **detects and removes personal data** — emails, phone numbers, US Social Security numbers, credit-card numbers, IP addresses, IBANs, and **names, organizations & locations** — from **text, CSV, or JSON that you supply**. Names, organizations and locations are found with a real **NER model (Microsoft Presidio + spaCy)**, so it catches genuine names and places rather than matching a fixed wordlist. It returns the cleaned data plus a detailed **redaction log** (what type of PII was found, where, and how many). It is **privacy-positive**: it *removes* personal data, it never scrapes or harvests it. > **Set the run memory to at least 2 GB.** The NER model (Presidio + spaCy `en_core_web_sm`) needs ~1–2 GB of RAM. The Actor ships with a default of 2048 MB; if you lower it in the Apify Console the run may fail to load the model. Run it in the cloud on the [Apify platform](https://apify.com) with API access, scheduling, and integrations, or call it from an AI agent over the Apify MCP. ### What does PII Redactor do? You give it content you are authorized to process — a support-ticket export, a CSV of form submissions, a JSON log dump, a block of pasted text — and it produces a **sanitized copy** with every detected piece of PII either: - **redacted** — replaced with a type tag such as `[EMAIL_REDACTED]`, or - **masked** — replaced with a **stable pseudonymous token** such as `[EMAIL_3f9a2b1c]`, where the *same* value always maps to the *same* token, so you keep referential integrity (join / group / count on the token) without ever exposing the raw value. Alongside the cleaned data you get a **redaction log** that records the PII **type, character position, and counts** — never the raw value — so you have an auditable trail of what was removed. #### What it detects | Type | Method | Notes | |------|--------|-------| | Email addresses | Presidio built-in | High precision | | Phone numbers | Presidio built-in (`phonenumbers`) | North-American + international formats | | US SSN | Pattern recognizer | Dashed / spaced `123-45-6789` form | | Credit cards | Presidio built-in + **Luhn check** | Validated to cut false positives | | IP addresses | Presidio built-in | IPv4 and IPv6 | | IBAN | Presidio built-in | International bank account numbers | | Names / people | **NER** (Presidio + spaCy) | Real names, e.g. `Aleksandr Vovchenko`, not just a wordlist | | Locations / places | **NER** (Presidio + spaCy) | Cities, countries, regions, e.g. `Berlin` | | Organizations | **NER** (Presidio + spaCy) — **opt-in** | Company names; excluded from the default set (see below) | | US postal addresses | Pattern recognizer | e.g. `1600 Pennsylvania Avenue` | > **Organizations are opt-in.** The small NER model over-eagerly tags label words and brands (e.g. `SSN`, `Visa`) as organizations, so `org` is left out of the default detection set to keep output high-precision. Add `org` to **PII types to detect** when you specifically want company names removed. ### Why use PII Redactor? - **Compliance & data minimization** — strip PII from datasets before sharing them with vendors, analysts, or LLM training/eval pipelines (GDPR / CCPA data-minimization). - **Safe AI pipelines** — sanitize prompts, logs, and documents before they hit a third-party model. - **Support / CRM exports** — clean ticket and chat exports for analytics without leaking customer identities. - **Referential integrity when you need it** — mask mode lets you analyze patterns (how many messages per customer) without knowing who the customer is. - **Real NER, not a wordlist** — names, organizations, and locations are detected by a Microsoft Presidio + spaCy model, so genuine (including non-English) names and places are caught, not just a fixed list. - **Low maintenance** — it processes only data you supply and never touches websites, so nothing breaks when a site changes. ### How to use PII Redactor 1. Open the Actor and go to the **Input** tab. 2. Choose a **Mode**: `redact` (remove) or `mask` (stable pseudonymous tokens). 3. Provide your content one of three ways: paste into **Input text**, give an **Input URL** to your own file, or pass an inline **Records** array. 4. (Optional) Restrict **PII types to detect**, restrict which **Fields to scan** for CSV/JSON, or set a **Mask salt** for per-tenant token isolation. 5. Click **Start** and download the cleaned results plus redaction log from the **Output** tab (JSON, CSV, Excel, or HTML). ### Input | Field | Type | Description | |-------|------|-------------| | `mode` | string | `redact` or `mask`. Default `redact`. | | `redactionTypes` | array | Subset of `email, phone, ssn, credit_card, ip, iban, name, location, address, org`. Empty = default set (everything except `org`). | | `inputFormat` | string | `text`, `csv`, or `json` — how to parse `inputText` / a fetched file. | | `inputText` | string | Inline content to clean. | | `inputUrl` | string | URL to **your own** text/CSV/JSON file (used only if no inline content). | | `records` | array | Inline array of strings/objects to clean directly (takes precedence). | | `fields` | array | For objects, restrict scanning to these field names. | | `maskSalt` | string | Optional secret salt for pseudonymous tokens in `mask` mode. | #### Input example ```json { "mode": "redact", "inputFormat": "text", "inputText": "Reach John Smith at john@acme.com or (212) 555-0147. SSN 078-05-1120." } ```` ### Output One dataset item per **non-empty** input record. Each item holds the cleaned content plus the redaction log. ```json { "index": 0, "mode": "redact", "redacted": "Reach [NAME_REDACTED] at [EMAIL_REDACTED] or [PHONE_REDACTED]. SSN [SSN_REDACTED].", "redactionCount": 4, "redactionsByType": { "name": 1, "email": 1, "phone": 1, "ssn": 1 }, "redactions": [ { "type": "name", "start": 6, "end": 16, "replacement": "[NAME_REDACTED]" }, { "type": "email", "start": 20, "end": 33, "replacement": "[EMAIL_REDACTED]" }, { "type": "phone", "start": 37, "end": 51, "replacement": "[PHONE_REDACTED]" }, { "type": "ssn", "start": 57, "end": 68, "replacement": "[SSN_REDACTED]" } ] } ``` For CSV/JSON object records, `redacted` mirrors the input object with each string field cleaned, and each redaction carries the `field` it was found in. You can download the dataset in **JSON, CSV, Excel, or HTML**. #### Output fields | Field | Type | Description | |-------|------|-------------| | `index` | integer | Position of the record in your input. | | `mode` | string | `redact` or `mask`. | | `redacted` | string | object | The cleaned content. | | `redactionCount` | integer | Total PII instances removed in this record. | | `redactionsByType` | object | Counts grouped by PII type. | | `redactions` | array | Per-match log: `type`, `start`, `end`, `replacement`, and `field` (for objects). Never contains the raw PII value. | ### Pricing / cost estimation This Actor is billed **pay-per-event**: you are charged **once per non-empty record processed** — never for blank rows, non-text values, or failed rows. The engine now runs a spaCy NER model (loaded once per run), so allow ~2 GB of memory and expect modestly higher per-record compute than a pure-regex pass; batching many records into one run amortizes the one-time model-load cost. See `PRICING.md` for the unit-economics rationale. ### Tips & advanced options - **Batch large jobs** via the `records` array or a CSV/JSON `inputUrl` — one run cleans thousands of records. - **Restrict `redactionTypes`** to only what you need for higher precision and clearer logs. - **Use `maskSalt` per customer/tenant** so pseudonymous tokens can't be correlated across tenants. - **`mask` vs `redact`**: choose `mask` when downstream analytics must still group/join by the (now anonymous) value; choose `redact` when you simply want the PII gone. ### FAQ, limitations & support - **Does it scrape anything?** No. It only processes data **you** supply (inline, or a URL to your own file). It never harvests personal data. - **Is the raw PII ever stored?** No. The redaction log records only the PII *type*, *position*, and *counts* — not the value. In `mask` mode the token is a one-way hash. - **How accurate is name / organization / location detection?** Structured types (email, phone, SSN, credit card, IP, IBAN) are high-precision. Names, organizations, and locations use a Microsoft Presidio + spaCy NER model — far better recall than a wordlist (it catches real, including non-English, names) but still statistical: it can miss lowercase or unusual mentions and occasionally over- or under-tag, especially organizations (which is why `org` is opt-in). The model is the small `en_core_web_sm` (chosen to keep the image and memory reasonable). Review the redaction log for sensitive workloads. - **Which formats?** Plain text, CSV (with header), and JSON (array or object). - **Feedback / bugs?** Use the **Issues** tab. Custom detectors or formats can be added on request. # Actor input Schema ## `mode` (type: `string`): How to handle detected PII. 'redact' replaces each match with a type tag like \[EMAIL\_REDACTED]. 'mask' replaces it with a STABLE pseudonymous token like \[EMAIL\_3f9a2b1c] — the same value always maps to the same token, so you keep referential integrity without exposing the raw data. ## `redactionTypes` (type: `array`): Which categories of PII to detect. Structured types (email, phone, SSN, credit card, IP, IBAN) are high-precision. Names, organizations, and locations are detected with a Microsoft Presidio + spaCy NER model, so real names/places are caught (not just a wordlist). Leave empty to detect the default set. NOTE: 'org' is OPT-IN — the small NER model over-tags label words and brand names as organizations, so it is excluded from the default set; add it explicitly if you need it. ## `inputFormat` (type: `string`): How to interpret 'Input text' or a fetched 'Input URL' file. 'text' treats the whole blob as one document. 'csv' parses rows (header required) and scans string cells. 'json' parses a JSON array (each item = one record) or object. ## `inputText` (type: `string`): The content to clean, pasted inline. Interpreted according to 'Input format'. Use this OR 'Input URL' OR 'Records'. ## `inputUrl` (type: `string`): A URL to YOUR OWN text/CSV/JSON file to clean. Fetched only when no inline content is given. This Actor never scrapes third-party sites — supply only data you are authorized to process. ## `records` (type: `array`): An inline JSON array to clean directly. Each item may be a string or an object; for objects, every string field is scanned (or only those listed in 'Fields to scan'). Takes precedence over 'Input text' / 'Input URL'. ## `fields` (type: `array`): For CSV/JSON object records, restrict scanning to these field names. Leave empty to scan every string field. ## `maskSalt` (type: `string`): Optional secret string used when 'mask' mode derives pseudonymous tokens. Use a per-customer salt to make tokens non-correlatable across tenants. Same salt + same value = same token. ## Actor input object example ```json { "mode": "redact", "redactionTypes": [ "email", "phone", "ssn", "credit_card", "ip", "iban", "name", "location", "address" ], "inputFormat": "text", "inputText": "Contact John Smith at john.doe@example.com or call (555) 123-4567. SSN 123-45-6789. Card 4111 1111 1111 1111. Server 192.168.1.100." } ``` # Actor output Schema ## `dataset` (type: `string`): No description # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "inputText": "Contact John Smith at john.doe@example.com or call (555) 123-4567. SSN 123-45-6789. Card 4111 1111 1111 1111. Server 192.168.1.100." }; // Run the Actor and wait for it to finish const run = await client.actor("nibble/pii-redactor").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "inputText": "Contact John Smith at john.doe@example.com or call (555) 123-4567. SSN 123-45-6789. Card 4111 1111 1111 1111. Server 192.168.1.100." } # Run the Actor and wait for it to finish run = client.actor("nibble/pii-redactor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "inputText": "Contact John Smith at john.doe@example.com or call (555) 123-4567. SSN 123-45-6789. Card 4111 1111 1111 1111. Server 192.168.1.100." }' | apify call nibble/pii-redactor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nibble/pii-redactor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "PII Redactor - Detect & Mask Emails, Phones, SSNs, Cards", "description": "Redact or pseudonymously mask PII (emails, phones, SSNs, credit cards, IPs, names, addresses) in user-supplied text, CSV, or JSON. Outputs cleaned data plus a redaction log.", "version": "0.1", "x-build-id": "W5T3i1xMjnxswJdEN" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nibble~pii-redactor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nibble-pii-redactor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nibble~pii-redactor/runs": { "post": { "operationId": "runs-sync-nibble-pii-redactor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nibble~pii-redactor/run-sync": { "post": { "operationId": "run-sync-nibble-pii-redactor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "mode": { "title": "Mode", "enum": [ "redact", "mask" ], "type": "string", "description": "How to handle detected PII. 'redact' replaces each match with a type tag like [EMAIL_REDACTED]. 'mask' replaces it with a STABLE pseudonymous token like [EMAIL_3f9a2b1c] — the same value always maps to the same token, so you keep referential integrity without exposing the raw data.", "default": "redact" }, "redactionTypes": { "title": "PII types to detect", "type": "array", "description": "Which categories of PII to detect. Structured types (email, phone, SSN, credit card, IP, IBAN) are high-precision. Names, organizations, and locations are detected with a Microsoft Presidio + spaCy NER model, so real names/places are caught (not just a wordlist). Leave empty to detect the default set. NOTE: 'org' is OPT-IN — the small NER model over-tags label words and brand names as organizations, so it is excluded from the default set; add it explicitly if you need it.", "items": { "type": "string", "enum": [ "email", "phone", "ssn", "credit_card", "ip", "iban", "name", "location", "address", "org" ], "enumTitles": [ "Email addresses", "Phone numbers", "US SSN", "Credit cards", "IP addresses (v4/v6)", "IBAN bank codes", "Names / people (NER)", "Locations / places (NER)", "US postal addresses", "Organizations (NER, opt-in)" ] }, "default": [ "email", "phone", "ssn", "credit_card", "ip", "iban", "name", "location", "address" ] }, "inputFormat": { "title": "Input format", "enum": [ "text", "csv", "json" ], "type": "string", "description": "How to interpret 'Input text' or a fetched 'Input URL' file. 'text' treats the whole blob as one document. 'csv' parses rows (header required) and scans string cells. 'json' parses a JSON array (each item = one record) or object.", "default": "text" }, "inputText": { "title": "Input text", "type": "string", "description": "The content to clean, pasted inline. Interpreted according to 'Input format'. Use this OR 'Input URL' OR 'Records'." }, "inputUrl": { "title": "Input URL (your own file)", "type": "string", "description": "A URL to YOUR OWN text/CSV/JSON file to clean. Fetched only when no inline content is given. This Actor never scrapes third-party sites — supply only data you are authorized to process." }, "records": { "title": "Records (inline array)", "type": "array", "description": "An inline JSON array to clean directly. Each item may be a string or an object; for objects, every string field is scanned (or only those listed in 'Fields to scan'). Takes precedence over 'Input text' / 'Input URL'." }, "fields": { "title": "Fields to scan (objects only)", "type": "array", "description": "For CSV/JSON object records, restrict scanning to these field names. Leave empty to scan every string field.", "items": { "type": "string" } }, "maskSalt": { "title": "Mask salt (optional)", "type": "string", "description": "Optional secret string used when 'mask' mode derives pseudonymous tokens. Use a per-customer salt to make tokens non-correlatable across tenants. Same salt + same value = same token." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```