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)

Balancing Quality and Efficiency in Private Clustering with Affinity Propagation. - Hannah Keller; Helen Möllering; Thomas Schneider 003; Hossein Yalame (IACR Cryptol. ePrint Arch., 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)

Differentially Private Selection from Secure Distributed Computing. - Ivan Damgård; Hannah Keller; Boel Nelson; Claudio Orlandi; Rasmus Pagh (IACR Cryptol. ePrint Arch., 2023)

Secure Noise Sampling for DP in MPC with Finite Precision. - Hannah Keller; Helen Möllering; Thomas Schneider 003; Oleksandr Tkachenko; Liang Zhao (IACR Cryptol. ePrint Arch., 2023)

MPC with Low Bottleneck-Complexity: Information-Theoretic Security and More. - Hannah Keller; Claudio Orlandi; Anat Paskin-Cherniavsky; Divya Ravi 001 (IACR Cryptol. ePrint Arch., 2023)

Distributed Differentially Private Data Analytics via Secure Sketching. - Jakob Burkhardt; Hannah Keller; Claudio Orlandi; Chris Schwiegelshohn (IACR Cryptol. ePrint Arch., 2024)

PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications. - Hilal Asi; Vitaly Feldman; Hannah Keller; Guy N. Rothblum; Kunal Talwar (IACR Cryptol. ePrint Arch., 2025)

Module Learning with Errors with Truncated Matrices. - Katharina Boudgoust; Hannah Keller (IACR Cryptol. ePrint Arch., 2025)

Differentially Private Quantiles with Smaller Error. - Jacob Imola; Fabrizio Boninsegna; Hannah Keller; Anders Aamand; Amrita Roy Chowdhury 001; Rasmus Pagh (CoRR, 2025)