Teaching at the Professorship of Cyber Trust

Information Technologies and Society (IT and Society)

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

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).

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: The first session of this course will be held on Friday, 26/04/2019 at 09:45am (no exercises on that day!)

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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.

Overview session (Vorbesprechung): Thursday, 31/01/2019, 14:00-15:00, room 01.08.033 (FMI building)

Kick-off meeting: Tuesday, 23/04/2019, 12:00-14:00, room 01.08.033 (FMI building)

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Seminar: Security and Privacy Economics

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

The seminar explores the nascent and growing field of the economics of privacy and security. Many security failures have economic causes. Systems are vulnerable when their defenders do not have sufficient incentives to invest in security technologies, for example, because they do not suffer the full consequences of their actions. At the same time, users’ personal and financial information has played a critical role in the monetization of attacks. But personal information has also become a commercial good for legitimate companies. Data is collected for countless purposes. Targeted advertisements, personalization, price discrimination as well as the creation and sale of background reports are enabled by the automated wholesale accumulation of users’ trail online and offline. In this seminar, we will investigate the economic incentives for security attacks and appropriate security defenses. A further objective is a better understanding of the current and future marketplace for personal information and the behavioral foundations of user privacy. We will discuss methods from the economic and behavioral sciences to contribute to a rigorous comprehension of the challenges and solution approaches for current privacy and security challenges.

Overview session (Vorbesprechung): Thursday, 31/01/2019, 11:00-12:00, room 01.08.033 (FMI building)

Kick-off meeting: Tuesday, 23/04/2019, 09:00-11:00, room 01.08.033 (FMI building)

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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): Thursday, 31/01/2019, 10:00-11:00, room 01.08.033 (FMI building)

Kick-off meeting: Wednesday, 24/04/2019, 13:00-16:00, room 01.08.033 (FMI building)

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Seminar: Trust in Automated Decision-Making

Course Instructor: Severin Engelmann

Why do humans have such trouble trusting algorithmic-decision making? The predictive strength of decision-making algorithms has led to their growing application in society, for example, in autonomous driving, online behavioral advertising, digital health, court decisions on recidivism, and credit scoring. There are even plans to deploy predictive algorithms as a replacement for human juries at the Olympics in 2022. The reason is simple: even rudimentary algorithmic models consistently outperform humans on various prediction tasks. However, research indicates that humans are reluctant to trust automated decision-making models. For example, almost 80% of Americans say they would not want to travel in an autonomous car because they don’t trust it. The same phenomenon holds for simpler applications of algorithmic models. The aim of this seminar is to explore the key factors that underlie human trust and distrust in algorithmic decision-making. Students will engage with a range of literature on human-machine interaction, deceptive and trust-enhancing interfaces, policy measures to create algorithmic trust, and human psychological dispositions of trust in automated decision-making. Each student will comprehensively review a paper to understand how it potentially informs academia, industry, or policy making. Overall, this seminar addresses an emergent scientific field and students are encouraged to focus on the implications of learning algorithms and novel data analytics methods on human trust from a variety of different perspectives. In order to complete the seminar successfully, students are required to prepare a presentation and hand in an 8-10-page report.

Overview session (Vorbesprechung): Wednesday, 06/02/2019, 16:00-17:00, 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 is held *online*, but will have an on-site kick-off at the beginning of the semester and presentation event at the end of the semester.

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