Convert Referential Anonymizer Online
Deterministic pseudonymization across multiple related CSVs — the same passphrase maps the same value to the same fake everywhere, so joins between anonymized files still work. Use Referential Anonymizer in your browser with no signup, no daily limit, and no backend processing for tool input.
Problem
You need to move data through Referential Anonymizer without writing a script or uploading the source file to another service.
What to do
Referential Anonymizer is useful when a format mismatch blocks the next step. Convert the data, inspect the preview, then continue into validation, cleanup, or export.
- 1Paste or upload the source data into Referential Anonymizer.
- 2Confirm the format options and preview the result.
- 3Copy or download the converted output.
Use the browser tool
Referential Anonymizer replaces identifying values with fake ones deterministically: the same input plus the same passphrase always produces the same output, across every file you process. That is what makes it different from ordinary masking — anonymise a customers file and an orders file with one passphrase and the customer ids still line up, so the joins between them survive. You get a dataset that is safe to share and still usable for testing, and no key table to protect.
People also ask
- How is this different from ordinary data masking?
- Ordinary masking replaces values independently, so the same customer id becomes two different fakes in two files and the join between them breaks. Deterministic mapping keeps them consistent, which is what makes the anonymised set still usable.
- What happens if I lose the passphrase?
- Previously anonymised files stay valid, but you cannot produce new files that join to them — a different passphrase gives entirely different mappings. Store it with the same care as a credential.
- Is this reversible?
- Not from the output alone. Reversal would require testing candidate inputs against the same passphrase, so protect the passphrase and treat the output as pseudonymised rather than fully anonymous.