The final grade will be determined based on multiple factors, including performance, the quality of the written report, and the presentation of the mini-project.

In this practical course, students will collaborate on a mini research project centered around privacy-enhancing technologies for processing medical data and genomes. Working in teams, they will explore and apply state-of-the-art privacy-preserving techniques to address key computational challenges in the context of medical data analysis. Throughout the course, students will delve into various privacy-enhancing methods, and share their knowledge with each other through concise presentations.

This practical course aims to equip students with a comprehensive understanding and practical skills in using privacy-enhancing technologies for secure processing of sensitive medical data, particularly genomic data. The course focuses on integrating machine learning algorithms within privacy-preserving frameworks. By the end of the course, students will gain an understanding of the challenges and privacy considerations in processing medical data, acquire knowledge of privacy-enhancing technologies, develop practical skills in implementing privacy-preserving machine learning algorithms for medical data and genomes, evaluate trade-offs between privacy preservation and data utility, apply privacy-enhancing technologies to real-world medical scenarios, collaborate on group projects, and present their findings. Upon completion, students will be well-prepared to navigate the complexities of privacy-enhancing technologies in the context of medical data, with a focus on genomics, while leveraging machine learning for valuable insights.

Wissenschaftliche Originalliteratur / Scientific publications