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