Structured-data redactor

What does the structured-data redactor produce?

Create a new dataset by retaining, masking, replacing or removing each field according to your explicit rules.

The working area above creates a separate transformed dataset in your browser. Review the supplied field rules, inspect the result and keep the original source backup private.

What do you need before you start?

• Authorize the disclosure purpose and review each retained field before transforming.
• Use UTF-8 CSV, text dataset JSON or a single-sheet text-cell XLSX file starting at A1. Each supports 2 MB, 5000 rows, 50 columns and 100000 cells. Expanded XLSX is limited to 10 MB. Formulas and macros are rejected.
• Assign Retain, Mask, Replace or Remove to every field. No unspecified field is retained automatically.

What information do you enter?

Select the actual source dataset and its complete approved field rules. Pseudonymization also requires a separate exact mapping dataset.

• Actual source dataset with distinct nonempty headings and text values.
• Approved field rules using exact Field and Action headings. Include Replacement for exact replacement text.
• Explicit approval of every field action before creating the transformed copy.

What you can download

Every result must identify its supplied facts, assumptions and unresolved items.

Fictional transformation review (PDF). Editable review (DOCX). Complete fictional pack (ZIP). Samples use the finalized renderer. Original files and mapping originals are omitted. Retained values still require disclosure review.

How does the structured-data redactor work?

1. 1
   Select the actual source

   Import the dataset and keep the original on this device. Download unchanged original bytes whenever needed.

2. 2
   Approve each field action

   Supply one rule per source field. Mask changes nonempty values to [REDACTED]. Replace applies the exact replacement, including an empty replacement. Remove omits the field.

3. 3
   Review the transformed copy

   Confirm every action and create a separate copy. Inspect output values, retained fields and row counts. The original files are not changed.

4. 4
   Keep separate copies

   Download the complete transformed result JSON free. Keep the private source backup separate. Purchase the finalized seven-file pack only after reviewing its disclosure copy.

Method and sources

Apply the steps above to the supported inputs. Use the listed source sections to review the rule basis, assumptions and limits.

• De-Identifying Government Datasets. Techniques and GovernanceA reference for distinguishing identifier transformations from a complete de-identification and disclosure review. Techniques, disclosure risk and governance.National Institute of Standards and Technology. Reviewed 2026-10-06.
• NIST Privacy Framework 1.0 CoreSelective disclosure and reducing observability, identification and linkability require an organization-approved privacy risk strategy. Control-P. Disassociated Processing CT.DP-P.National Institute of Standards and Technology. Reviewed 2026-10-06.

Common questions

What still needs review?

Field transformations do not detect sensitive content or establish anonymization. Retained fields, replacement text and free text need separate disclosure review.

Finalized files omit original source files and mapping originals. Retained and replacement values may still be sensitive. Browser backups contain originals and must remain private.

A missing rule, incomplete mapping, duplicate original, reused token or unsupported source prevents transformation. No partial output is supplied.

Related working records

The related workspace contains working administrative records. Open it for the related administrative task.

Related tools

Browse all privacy and data rights tools.

• Redaction verification toolCheck whether redactions remain recoverable in exported files. Try a free example. Review your inputs and result. No account or payment required.
• Document metadata scrubberRemove selected hidden properties from working copies. Try a free example. Review your inputs and result. No account or payment required.
• Pseudonymization utilityReplace identifiers using a controlled mapping. Try a free example, then review your inputs and the result. No account or payment required.
• Dataset minimization checkerCompare collected fields against a minimum. Try a free example, then review your inputs and the result. No account or payment required.
• Re-identification assessment preparationAssemble linkage risks and testing evidence. Try a free example, then review your inputs and the result. No account or payment required.