Teaching at the Professorship of Cyber Trust

Seminar: Data Analytics for Cybercrime and Undesirable Online Behaviors

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

Cybercriminal activities as well as other undesirable or malicious activities have increased in prevalence over the last decade. At the same time, the efforts and capabilities of industrial and academic researchers to understand these phenomena have made significant improvements. In this seminar, we will discuss a range of recent data-driven studies focusing, for example, on Spear-Phishing, Ransomware, Cybercriminal Marketplaces, Online Fraud etc., but also other challenges of societal interest such as Cyber-Bullying and Fake News. Each participant of the seminar will deeply engage with a key study to understand its focus, methodology, (data) limitations, and achievements. It is further expected to understand each work in the context of related studies, e.g., from security industry research labs. Participants of the seminar are expected to build on the literature to develop research objectives for further study.

Overview session (Vorbesprechung): Monday, 08/07/2019, 11:00-12:00, room 00.08.053 (FMI building)

Kick-off meeting: Friday, 18/10/2019, 13:00-16:00, room 01.08.033 (FMI building)

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Proseminar: Privacy

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

The seminar explores key facets of the concept of privacy. Questions that will be considered include the following: What is the history and origin of the concept of privacy? What are approaches to define and conceptualize privacy? What is the value of privacy seen from different perspectives such as economics and human rights? How is privacy currently regulated in different geographical regions (U.S., Europe, Germany), and across different business sectors? How do consumer express their desire for privacy and how do they act to protect or divulge personal information? How is privacy discussed in public, and by various stakeholders (e.g., companies)? What is the relationship of privacy to other important topics including identity, anonymity, and security? What technologies exist to protect and manage privacy, how do they work, and what do we know about their effectiveness? To address these questions a mix of theoretical, practice-oriented and policy literature and case examples will be used and evaluated by seminar participants.

Note: The Proseminar will not be part of the matching process for the Winter Semester 2019/2020. To enroll, please use TUMonline or contact the instructor via email.

Kick-off meeting: Monday, 21/10/2019, 13:00-15:00, room 01.08.033 (FMI building)

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Seminar: The Value of Privacy

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: Fairness in Machine Learning

Course Instructor: Felix Fischer

Machine learning allows prediction and classification of events and characteristics by training models based on labeled real world data. Whenever individuals are affected by decisions that are based on outcomes of machine learning models, concerns about discrimination and fairness arise. Decisions might be biased against people with certain protected characteristics like race or gender. Any discrimination that introduces bias into the training data, will likely be learned by the model. This might lead to unfair predictions that, for instance, deny loans or insurance to certain groups of people. Fair machine learning is an emerging research field that aims at detecting and mitigating such bias.

The seminar will investigate how fair machine learning is formalized and operationalized, given various fairness measures. This is either done by comprehensive literature study of the state-of-the-art, or by applying and evaluating bias mitigation techniques to given datasets and classification tasks. 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.

Overview session (Vorbesprechung): Wednesday, 17/07/2019, 14:30-15:30, room 01.08.033 (FMI building)

TUM Online: Course Description