Differentially Private Quantiles with Smaller Error
with Jacob Imola, Fabrizio Boninsegna, Anders Aamand, Amrita Roy Chowdhury, and Rasmus Pagh
In submission
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
with Hilal Asi, Vitaly Feldman, Guy N. Rothblum, and Kunal Talwar
In submission
Distributed Differentially Private Data Analytics via Secure Sketching
with Jakob Burkhardt, Claudio Orlandi, and Chris Schwiegelshohn.
In International Conference on Machine Learning (ICML 2025).
Module Learning with Errors with Truncated Seeds
with Katharina Boudgoust.
In International Conference on Post-Quantum Cryptography (PQCrypto 2025).
Differentially Private Selection from Secure Distributed Computing
with Ivan Damgård, Boel Nelson, Claudio Orlandi, and Rasmus Pagh.
In The Web Conference (WWW 2024).
Secure Noise Sampling for DP in MPC with Finite Precision
with 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
with 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
with Daniel Bernau, Günther Eibl, Philipp Grassal, and Florian Kerschbaum.
In Conference on Very Large Databases (VLDB 2022).
Balancing quality and Efficiency in Private Clustering with Affinity Propagation
with Helen Möllering, Thomas Schneider, and Hossein Yalame.
In Conference on Security and Cryptography (SECRYPT 2021).