Auto-Redact Personal Data in a PDF
or drop files here
Emails, phone numbers and ID numbers are spotted for you and boxed on the preview. You decide which boxes stay, and the detection runs as local code with no copy of your document sent out.
Detect, review, black out.
Drop the PDF
As soon as the file opens, every page of text is read and checked against ten patterns for personal data.
Review what was found
Matches appear as boxes on the preview, grouped by type with a count. Turn a category off, click a box to spare it, or drag in one the scan missed.
Apply and save
Selected boxes are burned into images of their pages and the underlying text is deleted. Save the copy when it is done.
Long documents, scattered details.
Hunting for every phone number in a forty-page export is slow and easy to get wrong. Automatic detection earns its keep in files like these.
Customer exports for a contractor
A list of orders or support tickets printed to PDF is full of emails and phone numbers. Strip them before an agency or developer sees the file.
Salary slips and Form 16
Indian payroll documents carry PAN, bank IFSC and sometimes Aadhaar. Hide them before a slip goes to a rental agent or a visa file.
KYC and loan paperwork
Identity numbers repeat on every page of an application pack. One pass finds each Aadhaar or Social Security number wherever it was typed.
European invoices and remittances
Sharing an invoice as an example or in a dispute? IBANs are validated before they are boxed, so reference numbers next to them stay readable.
Server logs and incident reports
Security write-ups quote IP addresses and user emails. Remove them before the report goes to a vendor or into a public post-mortem.
A second pair of eyes
After redacting by hand, open the result here. If the scan still turns up a card number on page 14, you caught it before anyone else did.
How a check digit tells a card from a lookalike
A pattern such as "thirteen to nineteen digits" would flag every tracking code and order reference in a document. Real card numbers, though, end in a check digit chosen so the whole number passes the Luhn test. Take 4242 4242 4242 4242. Starting from the right, double every second digit: each 4 becomes 8, each 2 stays 2. Add everything up and you get 8 × (8 + 2) = 80, which divides evenly by 10, so it passes. Change the last digit to 3 and the total is 81: rejected, and left unboxed.
A random sixteen-digit string passes Luhn only about one time in ten, so nine out of ten false alarms disappear before you see them. Aadhaar numbers use the Verhoeff scheme, which also catches swapped neighbouring digits, and IBANs use mod 97, which lets through only about one random string in ninety-seven. Strings of one repeated digit, like 0000 0000 0000 0000, are always skipped, even the ones that technically pass.
What detection cannot see. Names, street addresses, a number broken across two lines, and anything inside a scanned image. That is why each match is shown for review rather than applied blindly. For names, use the Find box on this page, or switch to Redact PDF to work entirely by hand.
| Identifier | Validation | Random string passes |
|---|---|---|
| Payment card | Luhn (mod 10) | About 1 in 10 |
| Aadhaar | Verhoeff | About 1 in 10 |
| IBAN | ISO 7064 mod 97 | About 1 in 97 |
| US SSN | Shape and banned ranges only | Needs the 123-45-6789 layout |
| PAN, IFSC | Exact letter and digit layout | No check digit to test |
When two patterns overlap, the more specific rule wins: a validated IBAN is reported once as an IBAN, never a second time as a phone number.
Questions answered.
What kinds of personal data does it detect?
How does it avoid boxing order numbers as card numbers?
Does it find names and street addresses?
Can I keep some matches and remove others?
Why did it find nothing in my scanned PDF?
Which phone number formats does it recognise?
Are all dates on the page treated as birth dates?
Is the black-out the same as in the manual Redact PDF tool?
Are the matches sent anywhere to be checked?
More tools you might need.
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