3 papers
cs.CR2021
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…
cs.CR2021
AggFT: Low-Cost Fault-Tolerant Smart Meter Aggregation with Proven Termination and Privacy
Günther Eibl, Sanaz Taheri-Boshrooyeh, Alptekin Küpçü
Smart meter data aggregation protocols have been developed to address rising privacy threats against customers' consumption data. However, these protocols do not work satisfactoril…
cs.CR2018
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…