mHealth Research Dataset Anonymization with anonym.plus

Prepare a mobile-health study set for sharing under research safeguards.

Dataset anonymization is the removal of personal data from a mobile-health study collection. GDPR Art. 89 lets such data serve research under safeguards. anonym.plus applies the strongest one — anonymization — on your device, so the study fields stay usable.

When this applies

A mobile study gathers app events, sensor readings, and survey answers from enrolled users. To share the collection with partners, identity must go.

How anonym.plus handles it

  1. Open the study set in anonym.plus on a local device.
  2. It scans ID columns and free-text fields alike.
  3. Sensor readings and survey scores stay in place.
  4. Swap the personal parts with the map turned off.
  5. Save the anonymous collection for sharing.

What you need to provide

PHI entity types detected

Categoryanonym.plus entity typeExample
NamesPERSONparticipant name → [SUBJECT_n]
ContactEMAIL_ADDRESSenrol email → [EMAIL]
IdentifiersIDdevice handle → [DEVICE]
NetworkIP_ADDRESSsync IP → [IP]
DatesDATE_TIMEenrol date → shifted [TIME]
Free textLOCATIONdiary city → [PLACE]

Compliance achieved

Anonymize mHealth research datasets offline — see plans & start free →

Limitations & cautions

Rich mobile data raises re-identification risk, since dense sensor traces can fingerprint a subject. Coarsen timestamps and geography, keep no re-link key, and review rare diary entries before you share the set.

Frequently asked questions

What does GDPR Art. 89 allow?

It lets data be processed for scientific research under safeguards like data minimisation. Full anonymization is the strongest such safeguard.

Are the sensor readings kept?

Yes. Readings and survey scores stay. Only the participant fields and free-text clues are removed.

Can subjects stay linkable across visits?

Yes. A steady code map gives each subject one alias, so visits join with no real identity kept.