Module Number MEDZ-4260 |
Module Title Secure Processing of Medical Data: Privacy-Enhancing Technologies in Practice |
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ECTS | 3 | |
Work load - Contact time - Self study |
Workload:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
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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. |
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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. |
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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. |
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Allocation of credits / grading |
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%) |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer / Other | Akgün | |
Literature | Wissenschaftliche Originalliteratur / Scientific publications |
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Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | BIO-PRAK, MEDZ-BIOMED, MEDZ-MEDTECH |