3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.CR2024★ 3 cited
Differentially Private Inductive Miner
Max Schulze, Yorck Zisgen, Moritz Kirschte +2
Protecting personal data about individuals, such as event traces in process mining, is an inherently difficult task since an event trace leaks information about the path in a proce…
cs.CR2023
S-BDT: Distributed Differentially Private Boosted Decision Trees
Thorsten Peinemann, Moritz Kirschte, Joshua Stock +2
We introduce S-BDT: a novel -differentially private distributed gradient boosted decision tree (GBDT) learner that improves the protection of single training data…
cs.CR2022
Private Blind Model Averaging - Distributed, Non-interactive, and Convergent
Moritz Kirschte, Sebastian Meiser, Saman Ardalan +1
Distributed differentially private learning techniques enable a large number of users to jointly learn a model without having to first centrally collect the training data. At the s…