Are You the Outlier? Identifying Targets for Privacy Attacks on Health Datasets

The identification of vulnerable records (targets) is an important step for many privacy attacks on protected health data. We implemented and evaluated three outlier metrics for detecting potential targets. Next, we assessed differences and similarities between the top-k targets suggested by the different methods and studied how susceptible those targets are to membership inference attacks on synthetic data. Our results suggest that there is no one-size-fits-all approach and that target selection methods should be chosen based on the type of attack that is to be performed.