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Daniel Bernau
Daniel Bernau
SAP Security Research
Verified email at sap.com - Homepage
Title
Cited by
Cited by
Year
Monte carlo and reconstruction membership inference attacks against generative models
B Hilprecht, M Härterich, D Bernau
Proceedings on Privacy Enhancing Technologies, 2019
1672019
Anonymization techniques to protect data
C Hebert, D Bernau, A Lahouel
US Patent 10,628,608, 2020
622020
Assessing differentially private deep learning with membership inference
D Bernau, PW Grassal, J Robl, F Kerschbaum
arXiv preprint arXiv:1912.11328, 2019
282019
Comparing local and central differential privacy using membership inference attacks
D Bernau, J Robl, PW Grassal, S Schneider, F Kerschbaum
IFIP Annual Conference on Data and Applications Security and Privacy, 22-42, 2021
242021
The influence of differential privacy on short term electric load forecasting
G Eibl, K Bao, PW Grassal, D Bernau, H Schmeck
Energy Informatics 1 (Suppl 1), 48, 2018
202018
Privacy-preserving outlier detection for data streams
J Böhler, D Bernau, F Kerschbaum
IFIP Annual Conference on Data and Applications Security and Privacy, 225-238, 2017
192017
Tracking privacy budget with distributed ledger
D Bernau, F Hahn, J Boehler
US Patent 10,380,366, 2019
162019
Differential privacy and outlier detection within a non-interactive model
J Boehler, D Bernau, F Kerschbaum
US Patent 10,445,527, 2019
142019
On the Privacy–Utility Trade-Off in Differentially Private Hierarchical Text Classification
D Wunderlich, D Bernau, F Aldà, J Parra-Arnau, T Strufe
Applied Sciences 12 (21), 11177, 2022
102022
Interpretability Framework for Differentially Private Deep Learning
D Bernau, PW Grassal, H Keller, M Haerterich
US Patent App. 17/086,244, 2022
92022
Assessing Differentially Private Variational Autoencoders under Membership Inference
D Bernau, J Robl, F Kerschbaum
IFIP Annual Conference on Data and Applications Security and Privacy, 3-14, 2022
72022
Quantifying identifiability to choose and audit in differentially private deep learning
D Bernau, G Eibl, PW Grassal, H Keller, F Kerschbaum
arXiv preprint arXiv:2103.02913, 2021
62021
Providing differentially private data with causality preservation
W Beskorovajnov, D Bernau
US Patent 10,423,781, 2019
62019
Selective access for supply chain management in the cloud
A Tueno, F Kerschbaum, D Bernau, S Foresti
2017 IEEE Conference on Communications and Network Security (CNS), 476-482, 2017
52017
Quantifying Identifiability to Choose and Audit ǫ in Differentially Private Deep Learning
D Bernau, G Eibl, PW Grassal, H Keller, F Kerschbaum
Proceedings of the Conference on Very Large Databases, 2021
42021
Privacy preserving smart metering
D Bernau, PW Grassal, F Kerschbaum
US Patent 10,746,567, 2020
42020
Differential privacy to prevent machine learning model membership inference
D Bernau, J Robl, PW Grassal, F Kerschbaum
US Patent 11,449,639, 2022
32022
Reconstruction and membership inference attacks against generative models
B Hilprecht, M Härterich, D Bernau
arXiv preprint arXiv:1906.03006, 2019
32019
Accurately identifying members of training data in variational autoencoders by reconstruction error
B Hilprecht, D Bernau, M Haerterich
US Patent 11,501,172, 2022
2022
Computer systems for detecting training data usage in generative models
M Haerterich, B Hilprecht, D Bernau
US Patent 11,366,982, 2022
2022
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