3 papers
cs.LG2026
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…
cs.CR2024
DPM: Clustering Sensitive Data through Separation
Johannes Liebenow, Yara Schütt, Tanya Braun +3
Clustering is an important tool for data exploration where the goal is to subdivide a data set into disjoint clusters that fit well into the underlying data structure. When dealing…
cs.CR2024
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…