AnonymizerTS

A TypeScript implementation of Microsoft Presidio for PII (Personally Identifiable Information) detection and anonymization, powered by Transformers.js v4.

Features

• 🔍 PII Detection: Automatically detect sensitive information in text

  • Named Entity Recognition (NER) using transformer models
  • Pattern-based recognition for emails, phone numbers, credit cards, SSNs, etc.

• 🛡️ Anonymization: Multiple anonymization strategies

  • Redact: Replace with entity type label (e.g., <PERSON>)
  • Replace: Substitute with custom values
  • Mask: Partial obfuscation (e.g., ***-**-1234)
  • Hash: One-way cryptographic transformation

• 🚀 Powered by Transformers.js: Run state-of-the-art NLP models directly in Node.js/Browser

• 📦 Zero Python Dependencies: Pure TypeScript implementation

• 🎯 Easy to Use: Simple, intuitive API

Supported Entity Types

• PERSON - Person names
• LOCATION - Geographic locations
• ORGANIZATION - Companies, institutions
• EMAIL_ADDRESS - Email addresses
• PHONE_NUMBER - Phone numbers
• CREDIT_CARD - Credit card numbers
• US_SSN - US Social Security Numbers
• IP_ADDRESS - IP addresses
• URL - Web URLs

Installation

npm install @siddicky/anonymizerts

Quick Start

import { PresidioAnalyzer, PresidioAnonymizer, OperatorType } from '@siddicky/anonymizerts';

async function anonymizeText() {
  // Initialize analyzer
  const analyzer = new PresidioAnalyzer({ useNER: true });
  await analyzer.initialize();

  // Analyze text
  const text = "John Smith's email is [email protected] and phone is (555) 123-4567";
  const results = await analyzer.analyze(text);

  // Anonymize
  const anonymizer = new PresidioAnonymizer({ type: OperatorType.REDACT });
  const anonymized = anonymizer.anonymize(text, results);
  
  console.log(anonymized.text);
  // Output: "<PERSON>'s email is <EMAIL_ADDRESS> and phone is <PHONE_NUMBER>"
}

anonymizeText();

Usage Examples

Example 1: Redact All PII

import { PresidioAnalyzer, PresidioAnonymizer, OperatorType } from '@siddicky/anonymizerts';

const analyzer = new PresidioAnalyzer({ useNER: true });
await analyzer.initialize();

const text = "Contact John at [email protected] or call 555-1234";
const results = await analyzer.analyze(text);

const anonymizer = new PresidioAnonymizer({ type: OperatorType.REDACT });
const anonymized = anonymizer.anonymize(text, results);

console.log(anonymized.text);
// "Contact <PERSON> at <EMAIL_ADDRESS> or call <PHONE_NUMBER>"

Example 2: Selective Masking

import { EntityType, OperatorType } from '@siddicky/anonymizerts';

const operators = new Map();

// Mask phone numbers (show only first 3 digits)
operators.set(EntityType.PHONE_NUMBER, {
  type: OperatorType.MASK,
  maskingChar: '*',
  charsToMask: 7,
  fromEnd: true,
});

// Hash SSNs
operators.set(EntityType.US_SSN, {
  type: OperatorType.HASH,
});

const anonymized = anonymizer.anonymize(text, results, operators);

Example 3: Custom Replacements

const operators = new Map();

operators.set(EntityType.PERSON, {
  type: OperatorType.REPLACE,
  newValue: '[REDACTED]',
});

operators.set(EntityType.EMAIL_ADDRESS, {
  type: OperatorType.REPLACE,
  newValue: '[email protected]',
});

const anonymized = anonymizer.anonymize(text, results, operators);

Example 4: Pattern-Only (No NER)

For faster processing without NER models:

const analyzer = new PresidioAnalyzer({ useNER: false });
// No need to call initialize() when NER is disabled

const results = await analyzer.analyze(text);

API Reference

PresidioAnalyzer

Constructor Options:

• useNER?: boolean - Enable NER-based recognition (default: true)
• modelName?: string - Hugging Face model name (default: 'Xenova/bert-base-NER')

Methods:

• initialize(): Promise<void> - Load NER model (required if useNER is true)
• analyze(text: string, entities?: EntityType[]): Promise<RecognizerResult[]> - Analyze text for PII

PresidioAnonymizer

Constructor:

• defaultOperator: OperatorConfig - Default anonymization operator

Methods:

• anonymize(text: string, results: RecognizerResult[], operators?: Map<EntityType, OperatorConfig>): AnonymizerResult

OperatorConfig

interface OperatorConfig {
  type: OperatorType;
  newValue?: string;          // For REPLACE operator
  maskingChar?: string;       // For MASK operator (default: '*')
  charsToMask?: number;       // For MASK operator
  fromEnd?: boolean;          // For MASK operator
}

Development

# Install dependencies
npm install

# Build
npm run build

# Run example
npm run example

Architecture

AnonymizerTS follows Microsoft Presidio's architecture:

1. Analyzer: Detects PII using multiple recognizers

   • NERRecognizer: Uses transformers.js for named entity recognition
   • PatternRecognizer: Regex-based patterns for structured data

2. Anonymizer: Applies anonymization operators to detected entities

   • Supports multiple strategies per entity type
   • Maintains text structure and readability

License

MIT

Credits

• Inspired by Microsoft Presidio
• Powered by Transformers.js
• Built with ❤️ by @siddicky