Health Data in an Open World
This highlights privacy risks in health data sharing, informing policy for data protection, but is incremental as it builds on prior similar studies.
The study tackled the problem of patient re-identification in de-identified open health datasets, demonstrating that individuals can be identified using mundane facts or publicly available information, with high confidence in some cases from a 10% sample dataset.
With the aim of informing sound policy about data sharing and privacy, we describe successful re-identification of patients in an Australian de-identified open health dataset. As in prior studies of similar datasets, a few mundane facts often suffice to isolate an individual. Some people can be identified by name based on publicly available information. Decreasing the precision of the unit-record level data, or perturbing it statistically, makes re-identification gradually harder at a substantial cost to utility. We also examine the value of related datasets in improving the accuracy and confidence of re-identification. Our re-identifications were performed on a 10% sample dataset, but a related open Australian dataset allows us to infer with high confidence that some individuals in the sample have been correctly re-identified. Finally, we examine the combination of the open datasets with some commercial datasets that are known to exist but are not in our possession. We show that they would further increase the ease of re-identification.