This project examines how re-identification risk varies within datasets
(particularly for individuals with unique or complex diagnostic profiles) and
identifies factors beyond average sample-level risk that contribute to
vulnerability. It aims to assess how data type, dataset structure, and
existing safeguards influence re-identification risk, with a focus on
informing responsible data sharing practices for research involving children
with complex diagnoses.