M. Sc. Moritz Kirschte

Moritz Kirschte, Sebastian Meiser, Saman Ardalan, and Esfandiar Mohammadi,
Private Blind Model Averaging – Distributed, Non-interactive, and Convergent, in 2026 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), to appear , Mä.2026.

Datei:

2211.02003

Moritz Kirschte, Thorsten Peinemann, Kari Kostiainen, Jan Wichelmann, Thomas Eisenbarth, and Esfandiar Mohammadi,
MammothDP: Differentially Private Boosted Decision Trees, hyperparameter-free and ready for Trusted Hardware, 2026.

Datei:

MammothDP-18.pdf

Niklas Zapatka, Moritz Kirschte, Sebastian Meiser, Bud Bruegger, Harald Zwingelberg, and Esfandiar Mohammadi,
Understanding Differential Privacy in Terms of Crowd Size Preservation, in Privacy Technologies and Policy, 14th Annual Privacy Forum (APF) , Cham, CH: Springer Nature Switzerland, Sep.2026. pp. 46-70.

DOI:	10.1007/978-3-032-35899-8_3
ISBN:	978-3-032-35899-8

Sebastian Meiser, Debajyoti Das, Moritz Kirschte, Esfandiar Mohammadi, and Aniket Kate,
Mixnets on a tightrope: Quantifying the leakage of mix networks using a provably optimal heuristic adversary, in 2025 IEEE Symposium on Security and Privacy (SP) , Los Alamitos, CA, USA: IEEE Computer Society, 2025. pp. 4457-4475.

ISBN:	979-8-3315-2236-0
Datei:	SP61157.2025.00233

Thorsten Peinemann, Moritz Kirschte, Joshua Stock, Carlos Cotrini, and Esfandiar Mohammadi,
S-BDT: Distributed Differentially Private Boosted Decision Trees, in Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24) , New York, NY, USA: Association for Computing Machinery, 2024. pp. 288–302.

DOI:	10.1145/3658644.3690301
ISBN:	979-8-4007-0636-3
Datei:	2309.12041

Max Schulze, Yorck Zisgen, Moritz Kirschte, Esfandiar Mohammadi, and Agnes Koschmider,
Differentially Private Inductive Miner, in 2024 6th International Conference on Process Mining (ICPM) , Curran Associates, Sep.2024. pp. 89-96.

DOI:	10.1109/ICPM63005.2024.10680684
ISBN:	979-8-3503-6503-0