About me
I am a PhD student in the cryptography group at Aarhus University, where I am advised by Claudio Orlandi. I am interested in differential privacy, secure multi-party computation, and intersections of the two.
In summer 2024, I completed an internship in Apple's privacy-preserving machine learning team. Before starting my PhD, I completed my master's at TU Darmstadt, advised by Thomas Schneider, in summer 2022.
Email: hkeller@cs.au.dk
Piquanε: Private Quantile Estimation in the Two-Server Model
Hannah Keller, Jacob Imola, Fabrizio Boninsegna, Rasmus Pagh, and Amrita Roy Chowdhury.
In submission.
Differentially Private Quantiles with Smaller Error
Jacob Imola, Fabrizio Boninsegna, Hannah Keller, Anders Aamand, Amrita Roy Chowdhury, and Rasmus Pagh.
In Conference on Neural Information Processing Systems (NeurIPS 2025).
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
Hilal Asi, Vitaly Feldman, Hannah Keller, Guy N. Rothblum, and Kunal Talwar.
In Conference on Neural Information Processing Systems (NeurIPS 2025).
Distributed Differentially Private Data Analytics via Secure Sketching
Jakob Burkhardt, Hannah Keller, Claudio Orlandi, and Chris Schwiegelshohn.
In International Conference on Machine Learning (ICML 2025).
Module Learning with Errors with Truncated Seeds
Katharina Boudgoust and Hannah Keller.
In International Conference on Post-Quantum Cryptography (PQCrypto 2025).
Differentially Private Selection from Secure Distributed Computing
Ivan Damgård, Hannah Keller, Boel Nelson, Claudio Orlandi, and Rasmus Pagh.
In The Web Conference (WWW 2024).
Secure Noise Sampling for DP in MPC with Finite Precision
Hannah Keller, Helen Möllering, Thomas Schneider, Oleksandr Tkachenko, and Liang Zhao.
In Conference on Availability, Reliability, and Security (ARES 2024).
MPC with Low Bottleneck Complexity: Information-Theoretic Security and More
Hannah Keller, Claudio Orlandi, Anat Paskin-Cherniavsky, and Divya Ravi.
In Conference on Information Theoretic Cryptography (ITC 2023).
Quantifying identifiability to choose and audit epsilon in differentially private deep learning
Daniel Bernau, Günther Eibl, Philipp Grassal, Hannah Keller, and Florian Kerschbaum.
In Conference on Very Large Databases (VLDB 2022).
Balancing quality and Efficiency in Private Clustering with Affinity Propagation
Hannah Keller, Helen Möllering, Thomas Schneider, and Hossein Yalame.
In Conference on Security and Cryptography (SECRYPT 2021).
Conference Talks
Other
Seminar talk, Visa Research Security Seminar, 2024
Seminar talk, Algorithms and Complexity Theory Seminar, Aarhus University, 2024
Research talk, Nordicrypt Meetup, 2023
Seminar talk, Cryptography Seminar, Aarhus University, 2022
Workshop talk, CrossFyre Workshop, 2021
Poster at PPML@CCS Workshop, 2021
Teaching Assistant, Cryptology, Fall 2023
Teaching Assistant, Systems Security, Spring 2023