Outcome Measure Dataset De-Identification with anonym.plus

Strip identity from score exports and keep the measures clean.

De-identifying an outcome dataset is the removal of data that names a subject from a score export. GDPR Art. 89 covers scientific research safeguards. anonym.plus runs on your device, so the measures stay while subjects are hidden.

When this applies

A research team pools symptom scores across sites. The export holds subject names, IDs, and dates, which must be stripped before analysis.

How anonym.plus handles it

  1. Open the export in anonym.plus on your device.
  2. Local OCR reads scanned score sheets too.
  3. It flags subject names, IDs, and dates.
  4. Review each flag and keep the score columns.
  5. Replace each value with a label, or remove it.
  6. Save the clean dataset. The source stays local.

What you need to provide

PHI entity types detected

Categoryanonym.plus entity typeExample
SubjectPERSONYusuf Demir → [SUBJECT_1]
Subject codeIDSUBJ-0142 → [CODE]
Score dateDATE_TIMEScored 05/22/2026 → [DATE]
SiteORGANIZATIONSite Berlin → [SITE]
EmailEMAIL_ADDRESSy.demir@uni.de → [EMAIL]
National IDNATIONAL_IDDE 904 117 → [ID]

Compliance achieved

Anonymize outcome measure datasets offline — see plans & start free →

Limitations & cautions

A small cell of rare scores can re-identify a subject. The tool removes direct identifiers. You still apply statistical care so an unusual row does not single out one person.

Frequently asked questions

What safeguards does Art. 89 expect?

It calls for measures like data minimization and pseudonymization for research use. Stripping direct identifiers is a core part of meeting that standard.

Can I keep a subject code?

Yes, if it is a random label, not a real ID. Keep the link table apart so the export alone cannot be reversed.

Does data cross a border?

No. The app runs locally, so no transfer to a third country occurs.