LOGAN: Membership Inference Attacks Against Generative Models
LOGAN: Membership Inference Attacks Against Generative Models
Abstract Generative models estimate the underlying distribution of a dataset to generate realistic samples according to that distribution. In this paper, we present the first membership inference attacks against generative models: given a data point, the adversary determines whether or not it was used to train the model. Our attacks …