Datamodel Distance: A New Metric for Privacy

Abstract

Recent work developing Membership Inference Attacks has demonstrated that certain points in the dataset are often in- trinsically easier to attack than others. In this paper, we intro- duce a new pointwise metric, the Datamodel Distance, and show that it is empirically correlated to and establishes a theoreti- cal lower bound for the success probability for a point under the LiRA Membership Inference Attack. This establishes a connection between the concepts of Datamodels and Member- ship Inference, and also gives new intuitive explanations for why certain points are more susceptible to attack than others. We then use datamodels as a lens through which to investigate the Privacy Onion Efect.