3 citations · 3 across the 2 of their papers we have counts for
6 papers
DP-Hype: Federated Differentially Private Hyperparameter Search
Johannes Liebenow, Thorsten Peinemann, Esfandiar Mohammadi
Tuning hyperparameters in federated machine learning can substantially impact model performance. When hyperparameters are tuned on sensitive data, privacy becomes an important chal…
Provable one-poison backdoor attacks on linear models and ReLU neural networks
Thorsten Peinemann, Paula Arnold, Sebastian Berndt +2
Backdoor poisoning attacks are a threat to machine learning models that are trained on data collected from untrusted sources; these attacks enable attackers to inject malicious beh…
Understanding the Theoretical Guarantees of DPM
Yara Schütt, Esfandiar Mohammadi
In this study, we conducted an in-depth examination of the utility analysis of the differentially private mechanism (DPM). The authors of DPM have already established the probabili…
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…
PrivAgE: A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices
Johannes Liebenow, Timothy Imort, Yannick Fuchs +4
Valuable insights, such as frequently visited environments in the wake of the COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data spread across edge-device…
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…