| Balancing quality and efficiency in private clustering with affinity propagation H Keller, H Möllering, T Schneider, H Yalame Cryptology ePrint Archive, 2021 | 13 | 2021 |
| Interpretability Framework for Differentially Private Deep Learning D Bernau, PW Grassal, H Keller, M Haerterich US Patent App. 17/086,244, 2022 | 7 | 2022 |
| 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 | 6 | 2021 |
| 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 | 4 | 2021 |
| Privacy-preserving clustering H Keller, H Möllering, T Schneider, H Yalame Gesellschaft für Informatik eV/FG KRYPTO, 2021 | 4 | 2021 |
| MPC with low bottleneck-complexity: Information-theoretic security and more H Keller, C Orlandi, A Paskin-Cherniavsky, D Ravi Cryptology ePrint Archive, 2023 | 2 | 2023 |
| Differentially Private Selection from Secure Distributed Computin I Damgård, H Keller, B Nelson, C Orlandi, R Pagh arXiv preprint arXiv:2306.04564, 2023 | | 2023 |
| Secure Noise Sampling for DP in MPC with Finite Precision H Keller, H Möllering, T Schneider, O Tkachenko, L Zhao Cryptology ePrint Archive, 2023 | | 2023 |
