15 citations · 16 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…