Scalable Membership Inference Attacks via Quantile Regression

The basic goal of privacy-preserving machine learning is to find models that are predictive on some underlying data distribution, without being disclosive of the particular data points on which they were trained. The simplest kind of attack that can be launched on a trained model--falsifying privacy guarantees--is a membership inference attack. A membership inference attack, informally, is a statistical test that is able to reliably determine whether a particular data point was included in the training set used to train the model or not. Almost all membership inference attacks are based on the observation that models tend to overfit their training sets in different ways. In particular, they tend to systematically predict higher confidence in the true labels of data points from their training set, compared to points drawn from the same distribution not in their training set. The confidence that a model places on the true label of a data-point is thus a natural test statistic to build a membership-inference hypothesis test around. A variety of recent methods [Shokri et al., 2017, Long et al., 2020, Sablayrolles et al., 2019, Song and Mittal, 2021, Carlini et al., 2022] are based around this idea, and aim to estimate the distribution of the test statistic (the confidence assigned to the true label of a datapoint) over the distribution of datapoints that were not used in training (and sometimes, Martin and Shuai are lead authors; all other authors are listed in alphabetical order.