data-detector is a general-purpose engine that detects and masks personal information (mobile phone numbers, social security numbers, email addresses, etc.) by country and information type, using a "pattern file-based + library + daemon (server)."

• 🌍 Global Support: Patterns organized by country (ISO2) and information type
• 🔍 Detection: Find PII in text using multiple patterns
• ✅ Validation: Validate text against specific patterns with optional verification functions
• 🔒 Redaction: Mask, hash, or tokenize sensitive information
• 🎲 Fake Data Generation: Generate fake PII for testing (CSV, JSON, Excel, Word, PowerPoint, PDF, Images, SQLite, XML, logs)
• 📚 Bulk Training Data: Generate large labeled datasets for ML training with complete metadata
• 🔄 Async Support: Full async/await API for concurrent processing
• 🚀 Multiple Interfaces: Library API, CLI, and HTTP/gRPC server
• ⚡ High Performance: p95 < 10ms for 1KB text (single namespace)
• 🔄 Hot Reload: Non-disruptive pattern reloading
• 📊 Observability: Prometheus metrics and health checks

git clone https://github.com/data-detector.git

pip install data-detector

See Installation Guide for more options.

from datadetector import Engine, load_registry

# Load patterns from directory
registry = load_registry(paths=["patterns/"])
engine = Engine(registry)

# Validate
is_valid = engine.validate("010-1234-5678", "kr/mobile_01")

# Find PII (searches all loaded patterns)
results = engine.find("My phone: 01012345678, email: [email protected]")

# Redact
redacted = engine.redact("SSN: 900101-1234567", namespaces=["kr"])
print(redacted.redacted_text)

Generate fake PII data for testing, demos, and development:

from datadetector import FakeDataGenerator, OfficeFileGenerator, ImageGenerator, PDFGenerator

# Create generator (use seed for reproducibility)
generator = FakeDataGenerator(seed=12345)

# Generate individual PII values
email = generator.from_pattern("comm/email_01")  # [email protected]
ssn = generator.from_pattern("us/ssn_01")  # 123-45-6789
phone = generator.from_pattern("kr/mobile_01")  # 010-1234-5678
aws_key = generator.from_pattern("comm/aws_access_key_01")  # AKIAIOSFODNN7EXAMPLE

# Generate files with fake data
generator.create_csv_file("users.csv", rows=1000, include_pii=True)
generator.create_json_file("users.json", records=500, include_pii=True)
generator.create_sqlite_file("users.db", records=1000, include_pii=True)
generator.create_log_file("app.log", lines=5000, log_format="apache")

# Generate Office files
office_gen = OfficeFileGenerator(generator)
office_gen.create_word_file("document.docx", paragraphs=10, include_pii=True)
office_gen.create_excel_file("data.xlsx", rows=500, include_pii=True)
office_gen.create_powerpoint_file("presentation.pptx", slides=10, include_pii=True)

# Generate images with embedded text
img_gen = ImageGenerator(generator)
img_gen.create_image_with_text("document.png", width=800, height=600, include_pii=True)
img_gen.create_screenshot_like_image("config.png", include_pii=True)

# Generate PDF files
pdf_gen = PDFGenerator(generator)
pdf_gen.create_pdf_file("document.pdf", pages=5, include_pii=True)
pdf_gen.create_pdf_invoice("invoice.pdf", include_pii=True)

Supported Pattern Types: emails, phone numbers, SSNs, credit cards, AWS/GitHub/Google API keys, IP addresses, coordinates, URLs, and more.

File Formats: CSV, JSON, SQLite, XML, logs (Apache/JSON/syslog), text, Word (.docx), Excel (.xlsx), PowerPoint (.pptx), PDF, PNG/JPEG images.

See examples/fake_data_quickstart.py and examples/fake_data_demo.py for complete examples.

Generate large labeled datasets for machine learning training:

from datadetector import BulkDataGenerator

# Create bulk generator
bulk_gen = BulkDataGenerator(seed=12345)

# Generate 10,000 labeled training records in JSONL format
bulk_gen.save_bulk_data_jsonl(
    "training_data.jsonl",
    num_records=10000,
    patterns_per_record=(3, 10)
)

# Generate binary classification pairs (has PII / no PII)
bulk_gen.save_detection_pairs(
    "detection_pairs.jsonl",
    num_pairs=5000,
    positive_ratio=0.7,
    format='jsonl'
)

# Generate with specific patterns only
specific_patterns = ["comm/email_01", "us/ssn_01", "comm/credit_card_visa_01"]
bulk_gen.save_bulk_data_json(
    "email_ssn_data.json",
    num_records=1000,
    include_patterns=specific_patterns
)

# Get statistics about generated dataset
records = bulk_gen.generate_bulk_labeled_data(num_records=100)
stats = bulk_gen.generate_statistics(records)
print(f"Total PII items: {stats['total_pii_items']}")
print(f"Pattern distribution: {stats['pattern_distribution']}")

Output Formats:

• JSONL - One JSON per line (streaming-friendly, ideal for ML pipelines)
• JSON - Complete dataset with global metadata
• CSV - Tabular format with JSON columns

Each Record Contains:

• text: Full text with embedded PII
• pii_items: List of PII with pattern IDs, values, and positions
• metadata: Number of PII items, patterns used, text length

Use Cases:

• Train PII detection models
• Create labeled datasets for supervised learning
• Binary classification training data
• Testing at scale (millions of records)
• Benchmarking detection performance

See examples/bulk_training_data_demo.py for comprehensive examples.

Full async/await API for concurrent processing:

import asyncio
from datadetector import AsyncEngine, load_registry

async def main():
    registry = load_registry()
    engine = AsyncEngine(registry)

    # Process single text
    result = await engine.find("Email: [email protected]")

    # Process multiple texts concurrently
    texts = ["Email: [email protected]", "Phone: 010-1234-5678", ...]
    results = await engine.find_batch(texts)

    # Concurrent validation and redaction
    validation = await engine.validate("010-1234-5678", "kr/mobile_01")
    redaction = await engine.redact("SSN: 123-45-6789")

asyncio.run(main())

Create and manage pattern files programmatically:

from datadetector import PatternFileHandler

# Create a new pattern file
PatternFileHandler.create_pattern_file(
    file_path="custom_patterns.yml",
    namespace="custom",
    description="My custom patterns",
    patterns=[{
        "id": "api_key_01",
        "location": "custom",
        "category": "token",
        "pattern": r"API-[A-Z0-9]{32}",
        "mask": "API-" + "*" * 32,
        "policy": {
            "store_raw": False,
            "action_on_match": "redact",
            "severity": "critical"
        }
    }]
)

# Add, update, or remove patterns
PatternFileHandler.add_pattern_to_file("custom_patterns.yml", new_pattern)
PatternFileHandler.update_pattern_in_file("custom_patterns.yml", "api_key_01", {...})
PatternFileHandler.remove_pattern_from_file("custom_patterns.yml", "api_key_01")

See YAML Utilities Documentation for complete guide.

The tokens.yml pattern file may use modified patterns (with rk_ prefix) during development to avoid triggering GitHub's push protection. Use the restoration utility to convert these back to real-world Stripe API key patterns:

# After installing via pip
data-detector-restore-tokens

# Or run directly
python restore_tokens.py

# Or as a module
python -m datadetector.restore_tokens

What it does:

• Converts fake rk_(live|test)_ patterns to real [sp]k_(live|test)_ Stripe patterns
• Updates examples to use proper sk_test_, sk_live_, pk_test_ prefixes
• Uses obviously fake example keys to avoid secret scanner detection

Security Note: All examples use FAKE keys like "EXAMPLEKEY" for security scanner compatibility. This is a defensive security tool - the patterns help detect real leaked credentials.

# Find PII
data-detector find --text "010-1234-5678" --ns kr

# Redact PII
data-detector redact --in input.log --out redacted.log --ns kr us

# Start server
data-detector serve --port 8080

# Start server
data-detector serve --port 8080

# Find PII
curl -X POST http://localhost:8080/find \
  -H "Content-Type: application/json" \
  -d '{"text": "010-1234-5678", "namespaces": ["kr"]}'

• Architecture - System architecture and design overview
• Quick Start Guide - Get started quickly
• Pattern Structure - Learn about pattern definitions
• Custom Patterns - Create your own patterns
• YAML Utilities - NEW! Programmatically create and manage pattern files
• Verification Functions - Add custom validation logic
• Configuration - Server and registry configuration
• API Reference - Complete API documentation
• Supported Patterns - Built-in pattern catalog
• Testing - Test suite documentation and coverage

• 📱 Phone numbers (KR, US, TW, JP, CN, IN)
• 🆔 National IDs (SSN, RRN, Aadhaar, etc.)
• 📧 Email addresses
• 🏦 Bank accounts & IBANs (with Mod-97 verification)
• 💳 Credit cards (Visa, MasterCard, Amex, etc.)
• 🛂 Passport numbers
• 📍 Physical addresses
• 🌐 IP addresses & URLs

Total: 60+ patterns across 7 locations (Common, KR, US, TW, JP, CN, IN)

See Supported Patterns for the complete list.

Patterns can include verification functions for additional validation beyond regex:

- id: iban_01
  category: iban
  pattern: '[A-Z]{2}\d{2}[A-Z0-9]{11,30}'
  verification: iban_mod97  # Validates IBAN checksum

Built-in verification functions:

• iban_mod97 - IBAN Mod-97 checksum validation
• luhn - Luhn algorithm for credit cards

You can also register custom verification functions. See Verification Functions for details.

• Latency: p95 < 10ms for 1KB text with single namespace
• Throughput: 500+ RPS on 1 vCPU, 512MB RAM
• Scalability: Handles 1k+ patterns and 1k+ concurrent requests

• No raw PII is logged (only hashes/metadata)
• TLS support for server
• Configurable rate limiting
• GDPR/CCPA compliant operations

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/ tests/
ruff check src/ tests/

# Validate patterns
python -c "from datadetector import load_registry; load_registry(validate_examples=True)"

# Build
docker build -t data-detector:latest .

# Run
docker run -p 8080:8080 -v ./patterns:/app/patterns data-detector:latest

MIT License - see LICENSE file for details.

Contributions are welcome! Please read our contributing guidelines and submit pull requests.

• 📖 Documentation
• 🐛 Issue Tracker
• 💬 Discussions

• Pattern Expansion: Support for additional countries like the EU, the UK, Canada, and Australia, as well as new PII types like Social Security Numbers, Vehicle Numbers, and Driver's License Numbers, will expand the usability of the pattern. Enhance the contribution guidelines to facilitate pattern additions by the community.

• Web UI/Test Tool: Currently, text must be submitted via the CLI or gRPC. Providing a UI that allows users to directly input patterns and view results, such as a web-based demo or a VS Code extension, will improve the user experience.

• Asynchronous/Streaming API: Adding an asyncio-based asynchronous API for high-speed log processing or data pipeline integration, or providing Kafka/Flink connectors, will facilitate application to large-scale systems.

• Automated Pattern Management: Maintaining the pattern catalog in a remote repository and implementing version control to automatically deploy pattern updates will improve operational convenience. Strictly defining the pattern format as a JSON schema will help prevent errors.

• Other Language Bindings: While gRPC allows calls from various languages, providing wrapper libraries for Node.js and Java would increase developer adoption.

• Monitoring and Deployment: In addition to the performance metrics presented in the README, adding benchmarks measuring memory usage and parallel processing performance in real environments, along with Kubernetes/Helm deployment examples and CI processes, would facilitate adoption by operations teams.