How to use Dataset Pseudonymizer
- Paste or select a UTF-8 CSV with a unique header row.
- Enter exact header names to replace, one per line.
- Keep mapping download off unless you have a separate protected place for that sensitive file.
- Run the substitutions and inspect both selected and unselected columns before sharing the CSV.
Example: Dataset Pseudonymizer
Replace repeated names and emails without changing account-number text.
Options
- CSV with a header row
- Up to 5 MiB, 20,000 data rows, 50 columns and 200,000 data cells. Every row must have the same width.
- Column names to pseudonymize
- Header names are matched exactly, including case and spaces. Each chosen column has its own mapping, so equal values in different columns are treated independently.
- Also download the sensitive original-to-token mapping
- The original-to-token file can reverse these substitutions. Enabling it creates sensitive output; store it separately from the shared dataset.
- Guard CSV spreadsheet formulas on download
- The export prefixes formula-triggering cells to reduce spreadsheet execution, including values in columns you did not pseudonymize. The displayed table retains those original strings.
Supported inputs and limits
Where your input is processed
This tool processes your input in this browser. Your text and files are not uploaded to UseFreeTools. Check this tool's limits for anything it may save on your device.
Pseudonyms are not anonymity
A stable token makes repeated records easier to compare, but unselected dates, account numbers or unusual combinations can still identify a person. Inspect the whole table before sharing. The mapping, if requested, directly links original values to tokens and must not travel with the supposedly protected dataset.
A new run makes a new mapping
Tokens are generated for the current job rather than restored from a saved mapping. Running the same CSV again can change every pseudonym. If downstream work requires stable tokens across files, this tool alone does not provide that workflow. Preserve an original copy separately when you need to repeat or audit the transformation.
Questions about Dataset Pseudonymizer
Is the output anonymous?
No. Other columns, patterns or an exported mapping can still identify people.
Do repeated identifiers receive the same replacement?
Yes, under the page’s stated mapping policy within the current run.
Should I share the mapping with the dataset?
Only when intended recipients need it. The mapping reconnects replacements to original identifiers.