Text Anonymizer — Redact Emails, Phones, Cards & IPs
Paste text and every email address, phone number, card number, IP address, IBAN, MAC address, URL, and API key in it gets replaced with a placeholder. Pick which kinds to remove, choose how the replacements look, and copy or download the cleaned version. The text never leaves your browser — which matters more here than on almost any other page, since the whole point is that the content is sensitive.
The usual reason to need this is mundane: pasting a log into a bug report, sharing an email thread with a colleague, putting a support ticket into a public issue, or handing a sample file to a contractor. In each case the useful part is the structure, not the personal details.
Numbered placeholders keep the text readable
Replacing every address with [EMAIL] destroys the thread: you can no longer tell whether two messages came from the same person. The numbered style keeps a consistent mapping, so the first address becomes [EMAIL_1] everywhere it appears and the second becomes [EMAIL_2]. The conversation still makes sense; the identities do not. The block style writes solid blocks instead, for when even the type of data is sensitive.
Card numbers are checked, not guessed
Any run of thirteen to nineteen digits looks like a card number, which is why simple tools flag order IDs and tracking codes. Candidates here are validated with the Luhn checksum — the same check payment systems use — so a number only counts as a card if the checksum passes. IP addresses are checked for valid octets for the same reason.
What this cannot find, and why that matters legally
Patterns only catch data with a fixed shape. Names, street addresses, job titles, free-text medical details, and anything else without a predictable form are invisible to this page — read the result before you share it. It is also worth knowing that under the GDPR, replacing identifiers with consistent placeholders is pseudonymisation, not anonymisation: if the mapping can be reversed, or the remaining text still identifies someone, the data is still personal data. Treat this as a tool that reduces exposure, not as a compliance guarantee.
For documents rather than text, use Redact PDF, which rasterises the covered area so the words underneath are really gone, and Remove PDF metadata for the hidden author and software fields. Photos carry their own identifiers — Remove EXIF data strips GPS coordinates and device details.
How to use Text Anonymizer
- Paste the text — a log, an email thread, a ticket, a CSV row, a config file.
- Pick the kinds of data to remove. Each chip shows how many items of that kind were found, so you can see what is in the text before deciding.
- Choose the replacement style: a plain label, a numbered label that keeps the same value consistent, or solid blocks.
- Read the result. Patterns cannot catch names or addresses, so check what is left before sharing it.
- Copy the cleaned text or download it as a .txt file.
Features
- Nine kinds of data — emails, phone numbers, card numbers, US SSNs, IBANs, IP addresses, MAC addresses, URLs, and API keys or tokens.
- Luhn-checked card detection — long digit runs are only treated as cards when the checksum passes, so order numbers survive.
- Consistent numbered placeholders — the same value keeps the same number, so a conversation stays readable after redaction.
- Counts per type — see how much of each kind the text contains before you replace anything.
- Token and secret detection — recognises OpenAI, GitHub, AWS, Google, and Slack key formats as well as JWTs.
- Entirely local — the text is processed in the page; nothing is uploaded, logged, or stored.
Frequently Asked Questions
How do I remove personal information from a block of text?
Paste it above, leave every category enabled, and copy the result. Emails, phone numbers, card numbers, IPs, IBANs, MAC addresses, URLs, and API keys are replaced with placeholders. Then read the output once more for names and addresses, which no pattern-based tool can detect.
Is the text uploaded anywhere?
No. The matching runs in your browser with ordinary JavaScript, there is no server call, and nothing is stored. You can confirm it by opening your browser’s network tab while you paste — or by disconnecting from the internet, since the page keeps working.
Does this make my data GDPR-anonymous?
No, and it is worth being precise about it. Replacing identifiers with consistent placeholders is pseudonymisation: under the GDPR, data that can still be linked back to a person — by you, or by anyone holding extra information — remains personal data. True anonymisation requires that re-identification is not reasonably possible, which depends on the whole text, not just the identifiers.
Why was a number not detected as a card?
Card numbers are validated with the Luhn checksum, so a mistyped or invented number will not match. That is deliberate: without the check, every order reference and tracking code in a log would be replaced as well.
Can it remove names?
No. Names have no fixed shape, and detecting them reliably needs a language model, which would mean a large download or a server. Rather than pretend otherwise, this page detects only what patterns can detect and says so — check the output for names yourself.
What about redacting a PDF or an image?
Use Redact PDF for documents: drawing a black box over text in a PDF viewer does not delete the text underneath, so that tool rasterises the page instead. For photos, Remove EXIF data strips the GPS coordinates and camera details stored inside the file.