Selective access for supply chain management in the cloud. - Anselme Tueno; Florian Kerschbaum; Daniel Bernau; Sara Foresti (CNS, 2017)

Privacy-Preserving Outlier Detection for Data Streams. - Jonas Böhler; Daniel Bernau; Florian Kerschbaum (DBSec, 2017)

The influence of differential privacy on short term electric load forecasting. - Günther Eibl; Kaibin Bao; Philip-William Grassal; Daniel Bernau; Hartmut Schmeck (Energy Inform., 2018)

Reconstruction and Membership Inference Attacks against Generative Models. - Benjamin Hilprecht; Martin Härterich; Daniel Bernau (CoRR, 2019)

Assessing differentially private deep learning with Membership Inference. - Daniel Bernau; Philip-William Grassal; Jonas Robl; Florian Kerschbaum (CoRR, 2019)

Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models. - Benjamin Hilprecht; Martin Härterich; Daniel Bernau (Proc. Priv. Enhancing Technol., 2019)

Comparing Local and Central Differential Privacy Using Membership Inference Attacks. - Daniel Bernau; Jonas Robl; Philip-William Grassal; Steffen Schneider 003; Florian Kerschbaum (DBSec, 2021)

On the privacy-utility trade-off in differentially private hierarchical text classification. - Dominik Wunderlich; Daniel Bernau; Francesco Aldà; Javier Parra-Arnau; Thorsten Strufe (CoRR, 2021)

Quantifying identifiability to choose and audit ε in differentially private deep learning. - Daniel Bernau; Günther Eibl; Philip-William Grassal; Hannah Keller; Florian Kerschbaum (CoRR, 2021)

Quantifying identifiability to choose and audit epsilon in differentially private deep learning. - Daniel Bernau; Günther Eibl; Philip-William Grassal; Hannah Keller; Florian Kerschbaum (Proc. VLDB Endow., 2021)

Assessing Differentially Private Variational Autoencoders Under Membership Inference. - Daniel Bernau; Jonas Robl; Florian Kerschbaum (DBSec, 2022)

Improved usability of differential privacy in machine learning: techniques for quantifying the privacy-accuracy trade-off. - Daniel Bernau (2022)