Mask and Redact Sensitive Data in Lists

Before sharing lists or documents, you often need to protect sensitive information. ListWrangler’s masking and redaction features let you replace personal data like email addresses and phone numbers with placeholder text, keeping your data safe while maintaining document structure.

What You’ll Learn

In this guide, you’ll discover how to:

• Mask email addresses in your lists
• Redact phone numbers
• Hide URLs and web addresses
• Replace sensitive data with custom placeholders
• Use patterns for custom data masking
• Prepare documents for safe sharing

Why Mask Sensitive Data?

Data masking is essential when you need to:

• Share sample data without exposing real information
• Create test datasets from production data
• Comply with privacy regulations (GDPR, CCPA, HIPAA)
• Publish examples without revealing personal details
• Audit documents for sensitive content
• Anonymize research data for analysis

Quick Masking with Pattern Mode

ListWrangler provides one-click masking for common data types:

Step 1: Open Pattern Mode

1. Click Find & Replace in the toolbar
2. Select the Pattern tab
3. Choose the data type to mask

Step 2: Select a Pattern

Choose from built-in patterns:

• Email - Masks email addresses
• Phone - Masks phone numbers
• URLs - Masks web addresses

Step 3: Choose “Replace” Action

1. Instead of “Extract Matches”, select Replace
2. Enter your replacement text (or use the default)
3. Click Replace All

Masking Email Addresses

Default Email Masking

Before:

Contact john.doe@example.com for support
Sales: sales@company.org, marketing@company.org

After (default mask):

Contact [EMAIL_REDACTED] for support
Sales: [EMAIL_REDACTED], [EMAIL_REDACTED]

Custom Email Replacement

Use a custom replacement for different needs:

Purpose	Replacement Text	Example Result
Privacy	[EMAIL]	Contact [EMAIL] for support
Placeholder	email@example.com	Contact email@example.com for support
Description	<email address>	Contact for support
Blank	(empty)	Contact for support

Masking Phone Numbers

Default Phone Masking

Before:

Call (555) 123-4567 for orders
Emergency: 800-555-1234
Mobile: 555.987.6543

After (default mask):

Call [PHONE_REDACTED] for orders
Emergency: [PHONE_REDACTED]
Mobile: [PHONE_REDACTED]

Custom Phone Replacements

Purpose	Replacement Text	Example Result
Generic	[PHONE]	Call [PHONE] for orders
Placeholder	(XXX) XXX-XXXX	Call (XXX) XXX-XXXX for orders
Description	<phone number>	Call for orders

Masking URLs

Default URL Masking

Before:

Visit https://www.example.com/account/settings
Download from https://cdn.example.com/file.pdf

After (default mask):

Visit [URL_REDACTED]
Download from [URL_REDACTED]

Custom URL Replacements

Purpose	Replacement Text	Example Result
Generic	[LINK]	Visit [LINK]
Placeholder	https://example.com	Visit https://example.com
Description	<web address>	Visit

Masking Multiple Data Types

To mask several types of sensitive data:

1. Start with one data type (e.g., emails)
2. Apply the mask
3. Move to the next type (e.g., phone numbers)
4. Apply that mask
5. Continue for all sensitive data types

Recommended Order

1. URLs first - They may contain email-like text
2. Emails second - Clear patterns
3. Phone numbers third - Multiple format variations
4. Custom patterns last - Specific to your data

Custom Data Masking with Patterns

For data types not covered by built-in patterns, use regex:

Masking Social Security Numbers

Pattern: \d{3}-\d{2}-\d{4} Replace with: XXX-XX-XXXX

Before: SSN: 123-45-6789 After: SSN: XXX-XX-XXXX

Masking Credit Card Numbers

Pattern: \d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4} Replace with: XXXX-XXXX-XXXX-XXXX

Before: Card: 1234-5678-9012-3456 After: Card: XXXX-XXXX-XXXX-XXXX

Masking IP Addresses

Pattern: \d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3} Replace with: [IP_REDACTED]

Before: Server: 192.168.1.100 After: Server: [IP_REDACTED]

Masking Names (Simple Approach)

For a list where each line is a name:

Pattern: ^.+$ Replace with: [NAME]

Before:

John Smith
Jane Doe

After:

[NAME]
[NAME]

Partial Masking Techniques

Sometimes you want to show part of the data while hiding the rest.

Partial Email Masking

Show domain, hide username:

Pattern: ([a-zA-Z0-9._%+-]+)@ Replace with: ***@

Before: john.doe@example.com After: ***@example.com

Partial Phone Masking

Show area code, hide rest:

Pattern: (\(\d{3}\)|\d{3})[-.\s]?\d{3}[-.\s]?\d{4} Replace with: $1-XXX-XXXX

Before: (555) 123-4567 After: (555)-XXX-XXXX

Last Four Digits Only

For credit cards or IDs:

Pattern: \d{12}(\d{4}) Replace with: XXXX-XXXX-XXXX-$1

Before: 1234567890123456 After: XXXX-XXXX-XXXX-3456

Best Practices for Data Masking

1. Know Your Data

Identify all types of sensitive information before masking.

2. Use Preview

Always preview replacements before applying to verify correct masking.

3. Verify Completeness

After masking, search for patterns that might have been missed.

4. Keep Original Secure

If you need the original data, save it securely before masking.

5. Document Your Process

Note what patterns you used for consistency across documents.

6. Consider Context

Masked data should still make sense in context.

Compliance Considerations

Different regulations have specific requirements:

GDPR (EU)

• Personal data must be anonymized or pseudonymized
• Masking should be irreversible for true anonymization
• Consider all personal identifiers

CCPA (California)

• Similar to GDPR requirements
• Focus on data that identifies individuals

HIPAA (Healthcare)

• Protected Health Information (PHI) requires specific handling
• 18 identifier categories must be addressed

Note: ListWrangler is a text processing tool. For compliance, consult with your legal and security teams.

Troubleshooting Masking Issues

Pattern Not Matching All Data

• Check for format variations
• Try broader patterns
• Use multiple passes for different formats

Replacing Too Much Text

• Make patterns more specific
• Use word boundaries (\b)
• Add context to patterns

Masked Data Breaking Document Structure

• Use placeholders that maintain length
• Keep formatting characters (brackets, etc.)

What’s Next?

Complete your data privacy workflow:

• Extract Data - Find all sensitive data first
• Custom Patterns - Build patterns for unique data
• Bulk Replace - Mask multiple patterns efficiently

Frequently Asked Questions

Is masked data recoverable?

No. Once you replace and save, the original data is gone. Always keep a backup of original data if needed.

Can I mask data in place?

Yes, masking replaces the text directly in your list. Make sure to preview first.

What about masking in specific columns?

ListWrangler processes all text. For column-specific masking, consider preprocessing to isolate the column.

Should I use the same replacement for all instances?

Usually yes, for consistency. But you can use different replacements for different data types (e.g., [EMAIL] vs [PHONE]).

How do I verify all sensitive data is masked?

After masking, use the extract feature to search for any remaining patterns. If matches are found, you have more data to mask.

Can I create a reusable masking workflow?

Save your custom patterns for reuse. For complex workflows, document the sequence of patterns you apply.