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): tbd

Kick-off meeting: tbd

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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.

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Seminar: Privacy-preserving Machine Learning (IN0014, IN2107, IN4990)

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.

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Seminar: Behavioral Insights in the Age of Big Data (IN0014, IN2107)

Course Instructor: Mo Chen, Ph.D.

Behavioral insights are “an inductive approach to policy-making combining fundamental insights from psychology, cognitive science, and social science with empirically-tested results to discover how humans actually make choices” (by OECD). There is a trend of governments and organizations applying behavioral insights to public policy to shape and influence behavior. In fact, there are now over 200 “nudge units” embedded in various government units all around the world. At the same time, the past decade witnessed a global interest in digital tools to influence behavior. Tools driven by the rapidly advancing technology development around big data as well as artificial intelligence (AI) are increasingly integrated in social governance. As a result, behavioral insights can now function as a policy-making tool to utilize the insights generated by big data, and the relationship between behavioral insights and big data is growing ever closer.

The Chinese social credit system (SCS) is a prime example for the digital transformation of society. While the SCS is unique in scope and scale, it does correspond to a trend of governments and organizations applying behavioral insights and big data to public policy to shape behavior. Therefore, it is important and instructive to take a closer look at the SCS, as the system could become a frontrunner regarding the engineering of social behavior.

Topics:
Possible topic areas include:
Behavioral insights for public policy
Personalized nudging and hyper nudges
Nudge units in different countries
Current state of the Chinese social credit system
Challenges of the Chinese social credit system
...
More topics are going to be presented and discussed during the meeting at the beginning of the semester.

Course objectives:
Understand behavioral insights in public policy making from an interdisciplinary point of view.
Learn about the Chinese social credit system and its potential challenges and impacts.
Become familiar with nudge units in different countries and their roles.

Prepare and write a scientific paper (English; 8-10 pages)
Conduct a presentation of your topic (English; 15 minutes + 10 minutes discussion)
Write a policy brief/short executive report (English; 1- 2 pages)

Requirement:
Strong interest in interdisciplinary work. Chinese language skills would be a plus.

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