PrivML Research Group
This page provides an overview on current activities of the PrivML research group I am coordinating. The topics are mainly (but not exclusively) centered around privacy-preserving machine learning.
Currently ongoing theses
|C. Müller||Master||Personalized Differential Privacy|
|W. Gu||Bachelor||Differential Private Synthetic Data Generation|
|T. Känel||Bachelor||Practical Evaluation of Neural Network Watermarking|
|J. Ihring||Master||Privacy Quantification Methods|
|O. Bouanani||Bachelor||Neural Network Architectural Choices for Privacy|
|D. Wang||Bachelor||Novel Methods for Neural Network Watermarking|
|D. Sosnovchyk||Bachelor||Synthetic Data Privacy Evaluation|
|M. Krüger||Bachelor||Application and Evaluation of Differential Privacy in Health Data Classification Tasks|
Currently, the following topics are available:
- Side Channel Attacks against Neural Networks (Master)
- Bias in AI (Bachelor/Master)
For a concrete description of the topics, please get in touch.