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CSV Anonymizer

Sanitize tabular data with smart PII detection

LogScrub processes CSV files with the same powerful pattern matching used for log files. It automatically detects and anonymizes PII in any column—emails, phone numbers, addresses, and more—while preserving the structure of your data.

📸 Screenshot placeholder: CSV file in editor with highlighted PII matches

Before & After Examples

Customer Database Export

Before Anonymization
id name email phone ip_address
1 John Smith john.smith@email.com +1 (555) 123-4567 192.168.1.100
2 Jane Doe jane.doe@company.org +1 (555) 987-6543 10.0.0.55
3 Bob Wilson bob.w@example.net +44 20 7123 4567 172.16.0.10
After Anonymization
id name email phone ip_address
1 [NAME-1] [EMAIL-1] [PHONE-1] [IP-1]
2 [NAME-2] [EMAIL-2] [PHONE-2] [IP-2]
3 [NAME-3] [EMAIL-3] [PHONE-3] [IP-3]

Transaction Log

Financial Data Anonymization
Before:
date,card_number,amount,merchant
2024-01-15,4111-1111-1111-1111,99.99,Amazon
2024-01-15,5500-0000-0000-0004,45.50,Starbucks
After:
date,card_number,amount,merchant
2024-01-15,[CC-1],99.99,Amazon
2024-01-15,[CC-2],45.50,Starbucks

Commonly Detected PII in CSV Files

Key Features

Consistency Mode

When the same email appears in multiple rows, it gets the same replacement every time. This preserves relationships in your data—if john@example.com becomes [EMAIL-1], you can still see all rows belonging to that user.

Consistent Replacements Across Rows
Before:
user_email,action
john@corp.com,login
jane@corp.com,view
john@corp.com,purchase
john@corp.com,logout
After:
user_email,action
[EMAIL-1],login
[EMAIL-2],view
[EMAIL-1],purchase
[EMAIL-1],logout

Syntax Validation

LogScrub validates your CSV structure and warns about common issues:

Large File Support

Process large CSV exports with virtual scrolling—only visible rows are rendered, keeping the interface responsive even with millions of rows.

Use Cases

Output Options

Ready to anonymize your CSV data?

Drop your CSV file into LogScrub to get started.

Launch LogScrub