Data Anonymizer

What this tool does

The Data Anonymizer replaces sensitive details in your data with safe, realistic placeholders so you can share a dataset without exposing real people. Paste any text — a CSV export, a JSON payload, a log file, a contact list — or drop a file, and the tool rewrites email addresses, phone numbers, payment-card-like numbers, long ID numbers and person names. It opens with a small sample of customer-style CSV already loaded, so you can see how every option behaves immediately.

The replacements look like genuine data — names such as “Jordan Smith”, emails on example.com — because anonymised data is most useful when it still resembles the real thing. All of the replacement names come from a short word list bundled with the tool. There is no external service and no Faker.js dependency.

Why and when you’d use it

Anonymised data solves a recurring problem: you need to share data, but the data contains people. A support analyst preparing a bug report wants to attach a real export without leaking customer emails. A marketer sending a sample list to an agency needs the structure intact but the contacts fake. A developer building a demo wants believable test data that is not anyone’s actual record. An operations lead sharing a spreadsheet in a training deck needs the figures but not the names.

In every one of those cases the safe move is to anonymise before the data leaves your control — and to do it with a tool that never sees the original. A server-side anonymiser would defeat the purpose: you would be uploading the very customer list you are trying to protect. Because this tool runs entirely in your browser, the sensitive original never travels anywhere.

If you need to reshape the cleaned data afterwards, CSV to Table and the Table Generator can convert it into other formats once the sensitive values are gone.

How to use it

1. Paste your text into the box, or drop a .csv, .txt, .json or similar file. The tool works on the raw text either way.
2. Under What to anonymise, switch on the categories you want — emails, phone numbers, card-like numbers, and optionally long ID runs.
3. For names: if the input is a CSV, tick the columns that hold names and they are replaced wholesale. For free text, turn on Find & replace specific names and list the exact names to anonymise.
4. Decide on Use consistent replacements. Leave it on to keep repeated values mapping to the same placeholder; turn it off for fully independent randomisation.
5. Read the counts to see how many emails, names, phones and cards were replaced, then Copy or Download the anonymised output.

Common pitfalls and tips

Automatic detection is pattern-based, so review the output before sharing it. A few specific things to watch: an unusual phone format — an extension, an international style the pattern does not expect — may be missed, so scan phone columns by eye. Names in free text are only replaced when you list them, so if a name appears in three spellings, list all three. The card detector looks for 13 to 16 digit runs; a longer account number could be caught as an ID instead, or a shorter one missed entirely.

Be aware too of the usual spreadsheet quirks. If your CSV has ZIP codes or SKUs with leading zeros, Excel may have already stripped them before you exported — that is a property of the file, not the anonymiser. And remember that anonymising the obvious fields is not the same as removing every trace: a free- text notes column can still mention a customer by name, so check those columns too.

Privacy

This is a privacy tool, so privacy is not a footnote — it is the design. Every step, detecting patterns, picking replacement names, masking card digits and serialising the result, happens as JavaScript on your own device. The data you paste or drop is never uploaded, never stored and never logged. That is the only honest way to build an anonymiser: the tool that strips your sensitive data should never be the tool that collects it.