Bachelor/Master Thesis Opportunities in Privacy-Preserving Medical Machine Learning

Arthur Ribeiro de Menezes, available immediately

Introduction/Background

 The research group Cognitive Security Technologies at Fraunhofer AISEC in Berlin is offering Bachelor and Master students the opportunity to conduct research in the field of medical machine learning with data privacy. The thesis will be conducted under the scope of a project focused on enabling the sharing of anonymized medical data through data synthesis powered by machine learning models.

 Topic Description

 Potential topics may include:

• Synthesizing medical data (e.g., MRI or tabular data) using advanced ML techniques such as diffusion models, normalizing flows, GANs and variational autoencoders (VAEs).
• Evaluating synthetic datasets for downstream utility.
• Conducting privacy audits of data synthesis methods, including membership inference and reconstruction attacks.
• Training machine learning models for data synthesis with differential privacy guarantees.

 Requirements

 Mandatory:

• Programming skills in Python.
• Foundational knowledge of machine learning and neural networks.

Desirable:

• Experience with machine learning frameworks (e.g., PyTorch, TensorFlow).
• Familiarity with privacy concepts (e.g., differential privacy, privacy attacks).

 

Classical HSP for abelian groups

Niklas Julius Müller, available immediately

The Hidden Supgroup Problem (HSP) searches for a Group H “hidden” by a function f, i.e., f(x) = f(x+h) for all h \in H. The algorithm of Shor solves this problem on a quantum computer for finite abelian groups and \Z. The aim of this Bachelor thesis would be to find and compare classical algorithms to the widely known quantum algorithms.

Requirements

• Quantenalgorithmen und Kryptoanalyse