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DPIA-Lite for Diabetes Research Data

This is a lightweight privacy impact checklist for local research use of IINTS-AF. A formal DPIA may still be required by a university, hospital, DPO, ethics board, or national law.

Processing Summary

Item Default SDK position
Processing location Local machine by default
Telemetry None by default
Data upload None unless the user explicitly uses a third-party service
Data types CGM values, pump events, carbs, simulated physiology, optional research metadata
Sensitive data Diabetes data can be health data under GDPR
Direct identifiers Not required and should not be imported
Genetic data Must be treated as highly sensitive if real human data is used

Data Minimisation Rules

  • Use pseudonymous subject_id values.
  • Do not include names, MRNs, addresses, phone numbers, e-mails, or exact dates of birth.
  • Keep raw private datasets outside the repository.
  • Export public artifacts with aggregate metrics and dataset fingerprints, not raw private records.
  • Prefer synthetic or public properly licensed datasets for demos.

Lawful Basis / Governance

The SDK does not choose a GDPR lawful basis for the user. The user or institution must determine it. For university or clinical research, check with the DPO/ethics board before processing real patient data.

Security Measures

  • Store private data in local protected folders.
  • Avoid cloud-synced folders for sensitive raw data unless approved.
  • Encrypt external drives when storing health data.
  • Keep Hugging Face repos private unless all artifacts are safe to publish.
  • Use MDMP certificates for data quality and provenance, not as a privacy guarantee.

Retention

  • Keep raw private data only as long as needed for the approved research purpose.
  • Delete temporary run folders when no longer needed.
  • Keep public model cards limited to metrics, source descriptions, and non-identifying manifests.

When a Formal DPIA Is Needed

Escalate to a formal DPIA/DPO review if:

  • identifiable health data is used;
  • genetic data is used;
  • data leaves the local machine;
  • a cloud service, API, or shared model repository receives health data;
  • the project is used in a clinical institution;
  • the project affects real people or care decisions;
  • large-scale patient datasets are processed.