Teaching at the Professorship of Cyber Trust

Information Technologies and Society (IN0025)

Course Instructor: Prof. Jens Grossklags, Ph.D.

The lecture offers an overview regarding the role of IT in society. Particular emphasis is given to the complex interactions between modern information and data analytics technologies and individual and societal privacy, and the safety and security of data of individuals and organizations. In addition, the economic impact of IT and the regulation of the impact of IT will be discussed (on concrete cases).

The lecture will primarily consist of a presentation. Opportunities for discussion and questions will be provided. The practice session will be used to further deepen the understanding of the lecture contents and will offer additional opportunities for discussion.

Lecture: Friday, 09:45 - 11:15 (room 2750, Karl Max von Bauernfeind Hörsaal, TUM downtown campus)
Exercises: Friday, 08:00 - 09:30 (room 2750, Karl Max von Bauernfeind Hörsaal, TUM downtown campus)

Note: There will be no lecture or exercise sessions in the lecture halls until further notice! Information and materials will be made available via Moodle.

TUM Online: Course Description

Information Management for Digital Business Models (IN8024)

Course Instructor: Prof. Jens Grossklags, Ph.D.

The lecture covers a diverse range of topics to address challenges in the area of information management for digital business models. We will further address issues related to organizing and leading Information management, and practical aspects of information management in companies and organizations.

The module consists of a lecture, an accompanying exercise track and an empirical research part.

Note: There will be no lecture or exercise sessions in the lecture halls until further notice! Information and materials will be made available via Moodle.

TUM Online: Course Description

Seminar: The Value of Privacy (IN0014, IN2107, IN4933)

Course Instructor: Severin Engelmann

What does privacy mean? What values do we address when we speak of privacy? How do these different values relate to each other? Is there a commercial value of privacy? Are privacy and security trade-offs? Overall, how can we protect the right to privacy in a digitalized society? Recently, in light of several global data breach scandals, such questions have become the subject of intense debate in the public, in academia, industry, and law. The aim of this seminar is to first explore the different conceptualizations of privacy from literature in law, sociology, philosophy, policy, and privacy enhancing technology. Second, students will review how current digital technologies, in particular, machine learning and big data methods in social media, online behavioural advertising, or intelligent personal assistants (and others) influence and shape our understanding of privacy. In order to complete the seminar successfully, students are required to prepare a presentation and, if desired, hand in an 8-10-page report.

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Seminar: Usable Security and Privacy (IN0014, IN2107, IN4932)

Course Instructor: Felix Fischer

During recent years, the requirement for secure and privacy preserving computer systems is reaching higher and higher priority. Luckily, a variety of technologies already exist, specifically designed to meet these requirements. However, most technologies were not designed keeping usability in mind. Consequently, important questions arise when integrating and applying these technologies: What are the implications on usability of computer systems? And vice versa, does usability have an impact on security and privacy? Are security and privacy requirements conflicting with (mostly more important) functional requirements? Do these conflicts lead to users rejecting secure systems? Is security and privacy versus usability an unavoidable trade-off? Currently, this trade-off tends to be either over-biased towards functionality and usability, or security and privacy. This seminar explores this problem and investigates state-of-the-art research on how to rebalance the trade-off. Moreover, based on related work, students will identify new problems, formulate research questions and justify their relevance. Students with exceptional and interesting ideas will be considered for theses or internships.

Preliminary meeting (Vorbesprechung): Tuesday, February 2, 2020, 13:30-14:30, room 01.08.033 (FMI building)

TUM Online: Course Description

Seminar: Privacy-preserving Machine Learning (IN0014, IN2107)

Course Instructor: Felix Fischer

Privacy-preserving machine learning is now at the forefront of academic research and the tech industry. For instance, Google has published several tools quite recently that aim at protecting the privacy of user data, which may be used to train machine learning models. The tools implement concepts like federated learning, differential privacy and secure multiparty computation. Those ensure that only the user has access to their data and that it can’t be leaked from trained models. However, privacy comes with costs of accuracy. Therefore, one of the biggest issues to solve in order to make these tools practicable is optimizing the trade-off between privacy and accuracy.

In this seminar students will investigate new concepts and implementations of privacy-preserving machine learning through comprehensive literature reviews. Findings need to be summarized and presented to the class. The seminar will have a kick-off meeting at the beginning of the semester, a mid-term meeting to evaluate progress and a presentation event at the end of the semester.

Preliminary meeting (Vorbesprechung): Tuesday, February 2, 2020, 15:00-16:00, room 01.08.033 (FMI building)

TUM Online: Course Description