15 citations · 19 across the 6 of their papers we have counts for
Showing 2019Show all
2 papers · 1 filter
cs.CR2019★ 15 cited
Assessing differentially private deep learning with Membership Inference
Daniel Bernau, Philip-William Grassal, Jonas Robl +1
Attacks that aim to identify the training data of public neural networks represent a severe threat to the privacy of individuals participating in the training data set. A possible…
cs.CR2019★ 3 cited
Reconstruction and Membership Inference Attacks against Generative Models
Benjamin Hilprecht, Martin Härterich, Daniel Bernau
We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without ass…