What you'll learn
- Explain why removing names and IDs does not anonymise health data
- Show where k-anonymity breaks down on small clinic and district datasets
- Describe what differential privacy promises, including epsilon in plain terms
Who it's for
Data protection officers, compliance leads, CTOs, CISOs, data engineers, health ministry teams and researchers who hold or publish patient data.
Hands-on
Re-identify “patients” in a small synthetic clinic dataset using only age, sex and district.
How the session runs
35 minutes of teaching, a 15-minute hands-on demonstration, then 10 minutes of questions. No programming is needed to follow along; technical attendees can run the optional notebook afterwards.
Part of the Data Privacy & AI pillar