Data Anonymizer

Names, emails, phone numbers, addresses, SSNs/IDs, credit cards, IP addresses, dates of birth — auto-detected by column header name, or manually assigned.

No. All anonymization runs in your browser. No data leaves your machine.

Same input always produces same anonymized output via a seeded PRNG. Preserves referential consistency across your dataset.

Yes, the Data Anonymizer is 100% free with no registration, no hidden fees, and no usage limits. All processing happens locally in your browser, ensuring complete privacy.

Yes, the Data Anonymizer is fully responsive and works on smartphones and tablets. You can use it on any device with a modern web browser -- no app download required.

No account or registration is needed. Simply open the Data Anonymizer in your browser and start using it immediately. There are no sign-up walls or usage restrictions.

Simply enter your input in the provided field, adjust any settings to your preference, and the tool will process it instantly. You can then copy the result to your clipboard or download it.

The Data Anonymizer works in all modern browsers including Chrome, Firefox, Safari, Edge, and Opera. For the best experience, use the latest version of your preferred browser.

Redacting removes a value entirely, replacing it with a placeholder like [REDACTED] so nothing remains to read. Masking hides part of a value while keeping its shape, the way a card number shows only the last four digits. Anonymizing goes further: it substitutes realistic but fake values — a different name, a fictional phone number, a shifted date — so the data still looks and behaves like the original but can no longer be traced to a real person. This tool does all three. You choose a rule per column, including Redact for full removal, Hash for a stable pseudonym, or category-specific replacement that regenerates format-correct fake names, emails, SSNs, and addresses. Pick the strategy that fits how the data will be used, then export the cleaned copy as CSV or JSON.

Turn on deterministic mode before you anonymize. By default each run draws fresh random values, so "John Smith" could become a different fake name every time. Deterministic mode uses a seeded generator (default seed 42) so the same input always produces the same output. That means "John Smith" becomes the identical fake name in every row and every file you process with that seed, and a hashed customer ID lines up across tables, keeping your foreign keys and joins intact. This referential consistency is essential when one person appears in several related datasets and you need the relationships to survive anonymization. Change the seed to generate a different but equally consistent mapping. Enable the deterministic toggle, set your seed, and process each file the same way to keep records linked.

Auto-detection works by matching your column header names against known patterns. A header called email, phone, ssn, dob, or address is recognized and flagged as a PII column before you do anything, and detected columns are highlighted so you can see your exposure at a glance. The categories it looks for include names, emails, phone numbers, postal addresses, SSNs and national IDs, credit card numbers, IP addresses, and dates of birth. Because detection is based on header names rather than reading every cell, an oddly named column may not be flagged automatically — but you can manually assign any rule to any column, so nothing is locked to the auto guess. Paste your CSV or JSON, review the highlighted columns, adjust assignments where needed, and anonymize with confidence that the right fields are covered.

No. The tool is designed to preserve structure: the output keeps the same columns, the same row count, and the same value formats as your original. Replacement values are format-correct, so a fake phone number still looks like a phone number, a substituted SSN keeps the right digit pattern, and a regenerated email is a valid address. For dates you can use the shift rule, which moves each value by a random offset of up to 30 days in either direction, preserving rough timing without the exact date. For numeric columns like salaries, the noise rule perturbs values by up to ten percent so the distribution stays realistic. This keeps anonymized data usable as test data with correct types and intact relationships. Use the before-and-after preview to confirm the shape held before you export.

The Hash rule converts a value to the first eight characters of its SHA-256 digest, computed in your browser with the Web Crypto API. Unlike Redact, which overwrites everything with [REDACTED] and discards the value entirely, hashing produces a stable pseudonym: the same input always hashes to the same short string, so you can still group, count, or join on that column without ever exposing the real identifier. Use Hash when you need to keep relationships — like linking orders to a customer ID across tables — but must not reveal the underlying value. Use Redact when the field carries no analytical value and should simply disappear, such as a free-text notes column with stray personal details. Assign Hash to your key columns and Redact to the rest, then export the result as CSV or JSON.