2023: Privacy-Friendly Notifications for the iOS TUM Campus App

Bachelor's theses

Anton Wyrowski



This thesis aims to enhance the TUM Campus iOS app by improving the user experience and providing additional functionality. The thesis presents a privacy-friendly grade notification system with smart scheduling and data-handling techniques, as well as a feature to calculate an estimated average grade. These enhancements enable the students to monitor their academic progress more conveniently and have the potential to improve the overall user experience of the app.

To construct the grade notification system, several building blocks were introduced, including a secure method of sending push notifications to the TUM Campus iOS app by encrypting the notification payload with an asymmetric key pair and mechanisms to use the student’s TUM API access token without storing it on the server. Subsequently, we introduce three approaches for the smart grade notification scheduling system, which we evaluated based on the number of network requests to the TUM API and the delay in notifying students of new grades. The first approach involves checking grades for each student individually. In contrast, the second and third approaches entail checking grades for all students by checking one student for each lecture and subsequently notifying all enrolled students. The third approach additionally introduces a new algorithm based on the Set Cover Problem to minimize the number of students required to check all lectures for new grades. The results indicate that all approaches are practical, with the third approach being the most efficient regarding network requests. However, the second approach may be the most efficient in terms of delay, which requires further testing in a real-world scenario.

The implementation of the estimated average grade requires access to additional information on TUM lectures, which is not provided by the TUM API. This thesis presents the development of a web crawler to obtain the ECTS points and weight of each lecture, from the TUM website, which are then stored in a database and updated periodically. Utilizing this information, the estimated average grade can be calculated for all student study programs. The evaluated results indicate that the estimated average grade has an average error of 5.5%. However, it should be noted that there can be deviations, especially for students who have taken special elective courses or study programs with special weighting rules, due to missing information on the TUM website. Furthermore, this thesis only considers a limited evaluation group of students, which may lead to further deviations when applied in a real-world scenario.

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Prof. Dr. Stephan Krusche

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