Reconstruction and Membership Inference Attacks against Generative Models

Understand

We present two information leakage attacks that outperform previous work on membership inference against generative models.

• The first attack allows membership inference without assumptions on the type of the generative model.
• Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records.
• The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy.