Employee Data Pseudonymizer
Replace names, employee numbers and email addresses with consistent aliases, and find out how identifiable the file still is afterwards.
Reads .xlsx and .csv. Everything, including the key, stays inside your browser.
The spreadsheet
Drop a spreadsheet here
An employee roster or export with a header row.
Why it is called a pseudonymizer and not an anonymizer
Because that is what it does. Taking the names out replaces the direct identifiers, and it does not make a dataset anonymous. In a two-hundred-person company, VP of Engineering, hired 2011, band 4 is one person whether or not their name is in the cell. NIST is explicit that masking personal information is not on its own sufficient de-identification, because the fields left behind can still be linked back to people.
So this tool does two things, and the second is the one worth having. It replaces the identifiers, and then it counts how many people in the result are still the only one with their combination of department, job title, hire date and everything else you kept. That number is usually higher than people expect.
Why the aliases need a key
The obvious approach is to hash the name, and it is the dangerous one. A plain hash is the same for everybody, so anybody holding your file and a list of your employees can hash the list and match it back in seconds. The aliases here are keyed to a secret only you have, which means the same secret reproduces the same aliases on a future file and nobody without it can reverse the mapping.
The mapping is not stored. Not here, not on a server, nowhere. That is also why losing the key means losing the ability to produce matching aliases again.