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Books and Theses
- 2022
[b1]Daniel Bernau:
Improved usability of differential privacy in machine learning: techniques for quantifying the privacy-accuracy trade-off. University of Stuttgart, Germany, 2022
Journal Articles
- 2021
- [j3]Daniel Bernau, Günther Eibl, Philip-William Grassal, Hannah Keller, Florian Kerschbaum:
Quantifying identifiability to choose and audit epsilon in differentially private deep learning. Proc. VLDB Endow. 14(13): 3335-3347 (2021) - 2019
- [j2]Benjamin Hilprecht, Martin Härterich, Daniel Bernau:
Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models. Proc. Priv. Enhancing Technol. 2019(4): 232-249 (2019) - 2018
- [j1]Günther Eibl
, Kaibin Bao, Philip-William Grassal, Daniel Bernau, Hartmut Schmeck:
The influence of differential privacy on short term electric load forecasting. Energy Inform. 1(S1) (2018)
Conference and Workshop Papers
- 2022
- [c4]Daniel Bernau, Jonas Robl, Florian Kerschbaum:
Assessing Differentially Private Variational Autoencoders Under Membership Inference. DBSec 2022: 3-14 - 2021
- [c3]Daniel Bernau, Jonas Robl, Philip-William Grassal, Steffen Schneider, Florian Kerschbaum:
Comparing Local and Central Differential Privacy Using Membership Inference Attacks. DBSec 2021: 22-42 - 2017
- [c2]Anselme Tueno, Florian Kerschbaum, Daniel Bernau, Sara Foresti:
Selective access for supply chain management in the cloud. CNS 2017: 476-482 - [c1]Jonas Böhler, Daniel Bernau, Florian Kerschbaum:
Privacy-Preserving Outlier Detection for Data Streams. DBSec 2017: 225-238
Informal and Other Publications
- 2022
- [i6]Daniel Bernau, Jonas Robl, Florian Kerschbaum:
Assessing Differentially Private Variational Autoencoders under Membership Inference. CoRR abs/2204.07877 (2022) - 2021
- [i5]Dominik Wunderlich, Daniel Bernau, Francesco Aldà, Javier Parra-Arnau, Thorsten Strufe:
On the privacy-utility trade-off in differentially private hierarchical text classification. CoRR abs/2103.02895 (2021) - [i4]Daniel Bernau, Günther Eibl, Philip-William Grassal, Hannah Keller, Florian Kerschbaum:
Quantifying identifiability to choose and audit ε in differentially private deep learning. CoRR abs/2103.02913 (2021) - 2019
- [i3]Benjamin Hilprecht, Martin Härterich, Daniel Bernau:
Reconstruction and Membership Inference Attacks against Generative Models. CoRR abs/1906.03006 (2019) - [i2]Daniel Bernau, Philip-William Grassal, Jonas Robl, Florian Kerschbaum:
Assessing differentially private deep learning with Membership Inference. CoRR abs/1912.11328 (2019) - 2018
- [i1]Günther Eibl, Kaibin Bao, Philip-William Grassal, Daniel Bernau, Hartmut Schmeck:
The Influence of Differential Privacy on Short Term Electric Load Forecasting. CoRR abs/1807.02361 (2018)
Coauthor Index
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