Assessing re-identification risk using synthetic data

One common strategy to share health data for secondary analysis while meeting increasingly strict privacy regulations is to de-identify it. To demonstrate that the risk of re-identification is acceptably low, re-identification risk metrics are used.

There has been a significant amount of research on developing estimators of this risk. Our recent publication in PLoS-ONE illustrates a novel risk estimator for sample-to-population attacks by creating a synthetic variant of the population dataset. This webinar will present the main findings of our publication including the derivation of the risk estimate, performance evaluations, and a case study using this estimator to perform de-identification on a COVID-19 dataset.