Module Number

MEDZ-4260
Module Title

Secure Processing of Medical Data: Privacy-Enhancing Technologies in Practice
ECTS 3
Work load
- Contact time
- Self study
Workload:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

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.

Content

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.

Objectives

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.

Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Akgün
Literature

Wissenschaftliche Originalliteratur / Scientific publications

Last offered unknown
Planned for Wintersemester 2023
Assigned Study Areas BIO-PRAK, MEDZ-BIOMED, MEDZ-MEDTECH