Hannah Keller

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 11

Top Topics

Differential Privacy

Affinity Propagation

Jensen Shannon Divergence

Top Venues

IACR Cryptol. ePrint Arch.

Proc. VLDB Endow.

Publications

Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh
Differentially Private Selection from Secure Distributed Computing. WWW (2024)
Hannah Keller, Helen Möllering, Thomas Schneider, Oleksandr Tkachenko, Liang Zhao
Secure Noise Sampling for DP in MPC with Finite Precision. ARES (2024)
Hannah Keller, Helen Möllering, Thomas Schneider, Oleksandr Tkachenko, Liang Zhao
Secure Noise Sampling for DP in MPC with Finite Precision. IACR Cryptol. ePrint Arch. 2023 (2023)
Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh
Differentially Private Selection from Secure Distributed Computing. CoRR (2023)
Hannah Keller, Claudio Orlandi, Anat Paskin-Cherniavsky, Divya Ravi
MPC with Low Bottleneck-Complexity: Information-Theoretic Security and More. ITC (2023)
Hannah Keller, Claudio Orlandi, Anat Paskin-Cherniavsky, Divya Ravi
MPC with Low Bottleneck-Complexity: Information-Theoretic Security and More. IACR Cryptol. ePrint Arch. 2023 (2023)
Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, Rasmus Pagh
Differentially Private Selection from Secure Distributed Computing. IACR Cryptol. ePrint Arch. 2023 (2023)
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) (2021)
Hannah Keller, Helen Möllering, Thomas Schneider, Hossein Yalame
Balancing Quality and Efficiency in Private Clustering with Affinity Propagation. SECRYPT (2021)
Hannah Keller, Helen Möllering, Thomas Schneider, Hossein Yalame
Balancing Quality and Efficiency in Private Clustering with Affinity Propagation. IACR Cryptol. ePrint Arch. 2021 (2021)
Daniel Bernau, Günther Eibl, Philip-William Grassal, Hannah Keller, Florian Kerschbaum
Quantifying identifiability to choose and audit ε in differentially private deep learning. CoRR (2021)