Data Anonymizer Skill | Agent Skills

Data Anonymizer

Detect and mask personally identifiable information (PII) in text documents and structured data. Supports multiple masking strategies and can process CSV files at scale.

Quick Start

from scripts.data_anonymizer import DataAnonymizer

# Anonymize text
anonymizer = DataAnonymizer()
result = anonymizer.anonymize("Contact John Smith at john@email.com or 555-123-4567")
print(result)
# "Contact [NAME] at [EMAIL] or [PHONE]"

# Anonymize CSV
anonymizer.anonymize_csv("customers.csv", "customers_anon.csv")

Features

PII Detection: Names, emails, phones, SSN, addresses, credit cards, dates
Multiple Strategies: Mask, redact, hash, fake data replacement
CSV Processing: Anonymize specific columns or auto-detect
Reversible Tokens: Optional mapping for de-anonymization
Custom Patterns: Add your own PII patterns
Audit Report: List all detected PII with locations

API Reference

Initialization

anonymizer = DataAnonymizer(
    strategy="mask",      # mask, redact, hash, fake
    reversible=False      # Enable token mapping
)

Text Anonymization

# Basic anonymization
result = anonymizer.anonymize(text)

# With specific PII types
result = anonymizer.anonymize(text, pii_types=["email", "phone"])

# Get detected PII report
result, report = anonymizer.anonymize(text, return_report=True)

Masking Strategies

text = "Email john@test.com, call 555-1234"

# Mask (default) - replace with type labels
anonymizer.strategy = "mask"
# "Email [EMAIL], call [PHONE]"

# Redact - replace with asterisks
anonymizer.strategy = "redact"
# "Email ***************, call ********"

# Hash - replace with hash
anonymizer.strategy = "hash"
# "Email a1b2c3d4, call e5f6g7h8"

# Fake - replace with realistic fake data
anonymizer.strategy = "fake"
# "Email jane@example.org, call 555-9876"

CSV Processing

# Auto-detect PII columns
anonymizer.anonymize_csv("input.csv", "output.csv")

# Specify columns
anonymizer.anonymize_csv(
    "input.csv",
    "output.csv",
    columns=["name", "email", "phone"]
)

# Different strategies per column
anonymizer.anonymize_csv(
    "input.csv",
    "output.csv",
    column_strategies={
        "name": "fake",
        "email": "hash",
        "ssn": "redact"
    }
)

Reversible Anonymization

anonymizer = DataAnonymizer(reversible=True)

# Anonymize with token mapping
result = anonymizer.anonymize("John Smith: john@test.com")
mapping = anonymizer.get_mapping()

# Save mapping securely
anonymizer.save_mapping("mapping.json", encrypt=True, password="secret")

# Later, de-anonymize
anonymizer.load_mapping("mapping.json", password="secret")
original = anonymizer.deanonymize(result)

Custom Patterns

# Add custom PII pattern
anonymizer.add_pattern(
    name="employee_id",
    pattern=r"EMP-\d{6}",
    label="[EMPLOYEE_ID]"
)

CLI Usage

# Anonymize text file
python data_anonymizer.py --input document.txt --output document_anon.txt

# Anonymize CSV
python data_anonymizer.py --input customers.csv --output customers_anon.csv

# Specific strategy
python data_anonymizer.py --input data.csv --output anon.csv --strategy fake

# Generate audit report
python data_anonymizer.py --input document.txt --report audit.json

# Specific PII types only
python data_anonymizer.py --input doc.txt --types email phone ssn

CLI Arguments

| Argument | Description | Default | |----------|-------------|---------| | --input | Input file | Required | | --output | Output file | Required | | --strategy | Masking strategy | mask | | --types | PII types to detect | all | | --columns | CSV columns to process | auto | | --report | Generate audit report | - | | --reversible | Enable token mapping | False |

Supported PII Types

| Type | Examples | Pattern | |------|----------|---------| | name | John Smith, Mary Johnson | NLP-based | | email | user@domain.com | Regex | | phone | 555-123-4567, (555) 123-4567 | Regex | | ssn | 123-45-6789 | Regex | | credit_card | 4111-1111-1111-1111 | Regex + Luhn | | address | 123 Main St, City, ST 12345 | NLP + Regex | | date_of_birth | 01/15/1990, January 15, 1990 | Regex | | ip_address | 192.168.1.1 | Regex |

Examples

Anonymize Customer Support Logs

anonymizer = DataAnonymizer(strategy="mask")

log = """
Ticket #1234: Customer John Doe (john.doe@company.com) called about
billing issue. SSN on file: 123-45-6789. Callback number: 555-867-5309.
Address: 123 Oak Street, Springfield, IL 62701.
"""

result = anonymizer.anonymize(log)
print(result)
# Ticket #1234: Customer [NAME] ([EMAIL]) called about
# billing issue. SSN on file: [SSN]. Callback number: [PHONE].
# Address: [ADDRESS].

GDPR Compliance for Database Export

anonymizer = DataAnonymizer(strategy="hash")

# Consistent hashing for joins
anonymizer.anonymize_csv(
    "users.csv",
    "users_anon.csv",
    columns=["email", "name", "phone"]
)

anonymizer.anonymize_csv(
    "orders.csv",
    "orders_anon.csv",
    columns=["customer_email"]  # Same hash as users.email
)

Generate Test Data from Production

anonymizer = DataAnonymizer(strategy="fake")

# Replace real PII with realistic fake data
anonymizer.anonymize_csv(
    "production_data.csv",
    "test_data.csv"
)

# Test data has same structure but fake PII

Dependencies

pandas>=2.0.0
faker>=18.0.0

Limitations

Name detection may miss unusual names
Address detection works best for US formats
Custom patterns may be needed for domain-specific PII
Fake data replacement doesn't preserve exact format

Agent Skills: Data Anonymizer

Skill Files