Data Anonymizer

Paste or upload text, JSON, CSV, or HTML and anonymize sensitive data in seconds. This Data Anonymizer detects names, email addresses, phone numbers, IP addresses, physical addresses, company names, and dates of birth. Choose placeholder replacement, partial masking, or realistic fake data while keeping your original formatting when needed.

Data types to anonymize
Replacement log

Unique replacements: 0

Type Original Replacement Count
No replacements yet.

About the Data Anonymizer Tool

The Data Anonymizer helps you remove sensitive information from text, JSON, CSV, and HTML directly in your browser. It can detect common personal data patterns and replace them with placeholders, masked values, or realistic fake data so your output is safer to share for testing, demos, documentation, or public publishing.

⭐ What this data anonymizer detects

  • Names
  • Email addresses
  • Phone numbers
  • IP addresses
  • Physical addresses
  • Company names
  • Dates of birth
  • Custom words and custom regex patterns

⚙️ Anonymization modes

  • Replace with placeholders: turns matches into labels like [EMAIL_1] and [NAME_2].
  • Mask partially: keeps part of the original value visible, for example j***@domain.com.
  • Generate realistic fake data: swaps detected values with believable synthetic replacements.

🔎 How to use this data anonymizer

  1. Paste data into the input area or upload a supported file.
  2. Select an anonymization mode.
  3. Choose which data types you want to anonymize.
  4. Optionally add custom words or regex patterns.
  5. Keep formatting enabled if your input is JSON or HTML and structure matters.
  6. Click Anonymize data and copy or download the result.

✅ Example transformation

Input:

John Smith
john.smith@company.com
+31 6 1234 5678
123 Main Street, NY 10001

Output (placeholder mode):

[NAME_1]
[EMAIL_1]
[PHONE_1]
[ADDRESS_1]

⚠️ Important notes

  • This tool uses pattern-based detection. Always review output before publishing.
  • All processing runs client-side in your browser, with no server upload required.
  • Complex edge cases can require custom regex for best anonymization accuracy.

💡 Best practices for safer sharing

  • Enable all relevant data type checkboxes for broader coverage.
  • Add project-specific terms in the custom words field.
  • Use custom regex for internal IDs, account numbers, or proprietary formats.
  • Do a final manual scan to validate no sensitive details remain.

Frequently Asked Questions

The Data Anonymizer scans your input and replaces sensitive information such as names, emails, phone numbers, IP addresses, physical addresses, company names, and dates of birth.

You can paste or upload text, JSON, CSV, and HTML content. The tool attempts to detect the format automatically.

You can replace with placeholders, mask values partially, or generate realistic fake data for detected fields.

Yes. You can enable or disable anonymization per type, including names, emails, phones, IPs, addresses, company names, and dates of birth.

Yes. Add comma-separated custom words and optional regex patterns (one per line) to detect and anonymize organization-specific values.

Keep formatting preserves your original spacing and line structure as much as possible, which is especially useful for JSON and HTML content.

No. The Data Anonymizer runs entirely in your browser, so your data stays local on your device.

Yes. Repeated values are replaced consistently within the same anonymization run to keep references readable in the output.

Yes. You can copy the output or use Download .txt to save the anonymized result as a text file.

No automated tool can guarantee perfect anonymization in every scenario. Always review the final output before sharing or publishing.

Yes. Fake data mode is useful for generating realistic-looking anonymized datasets for QA, staging environments, demos, and examples.

The tool can process large inputs, but speed depends on your browser and device resources because everything runs client-side.