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We present two information leakage attacks that outperform previous work onmembership inference against generative models. The first attack allowsmembership inference without assumptions on the type of the generative model.Contrary to previous evaluation metrics for generative models, like KernelDensity Estimation, it only considers samples of the model which are close totraining data records. The second attack specifically targets VariationalAutoencoders, achieving high membership inference accuracy. Furthermore,previous work mostly considers membership inference adversaries who performsingle record membership inference. We argue for considering regulatory actorswho perform set membership inference to identify the use of specific datasetsfor training. The attacks are evaluated on two generative model architectures,Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs),trained on standard image datasets. Our results show that the two attacks yieldsuccess rates superior to previous work on most data sets while at the sametime having only very mild assumptions. We envision the two attacks incombination with the membership inference attack type formalization asespecially useful. For example, to enforce data privacy standards andautomatically assessing model quality in machine learning as a service setups.In practice, our work motivates the use of GANs since they prove lessvulnerable against information leakage attacks while producing detailedsamples.

Reconstruction and Membership Inference Attacks against Generative Models