3 citations · 3 across the 1 of their papers we have counts for
3 papers
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…