Showing cs.CRShow all
2 papers · 1 filter
cs.CR2022
Assessing Differentially Private Variational Autoencoders under Membership Inference
Daniel Bernau, Jonas Robl, Florian Kerschbaum
We present an approach to quantify and compare the privacy-accuracy trade-off for differentially private Variational Autoencoders. Our work complements previous work in two aspects…
cs.CR2019
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…