3 citations · 3 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
Quantifying identifiability to choose and audit in differentially private deep learning
Daniel Bernau, Günther Eibl, Philip W. Grassal +2
Differential privacy allows bounding the influence that training data records have on a machine learning model. To use differential privacy in machine learning, data scientists mus…
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…
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…
The Influence of Differential Privacy on Short Term Electric Load Forecasting
Günther Eibl, Kaibin Bao, Philip-William Grassal +2
There has been a large number of contributions on privacy-preserving smart metering with Differential Privacy, addressing questions from actual enforcement at the smart meter to bi…