Information and Communication Security: Infrastructures, Technologies and Business Models

Basic Information

Type of Lecture:	Lecture with practical component
Course:	Master
Hours/Week:	3
Credit Points:	6
Language:	English
Term:	Winter 2026/2027
Lecturers:	Prof. Dr. Kai Rannenberg  Md Arman Khan Kaveri Ramesh Dabhade Mafi Islam
Email:	This email address is being protected from spambots. You need JavaScript enabled to view it.

Content of the Course

Description: Learning Goals and Competencies

• Basic understanding of security and privacy technologies
• Basic understanding of technological and legal frameworks to prevent digital privacy threats to individuals
• Basic understanding of security engineering processes, and security risk management
• Understanding the importance of information security at organizational level

Descripton of course:

The “Information and Communication Security” lecture series provides a general introduction to the topic of information and communication security. Students will be qualified to identify security challenges that come along with using and employing information and communication systems, and to identify and apply adequate means and methods to solve them.

Emphasis will be put on the organisational and technical set-up of infrastructures, also including aspects of risk management and profitability analysis of security concepts and technologies. Further, students will be qualified to identify and assess security risks, in order to develop and establish appropriate security strategies.

Contents presented in the lecture will also be discussed in the context of international standardisation and regulation activities in the domain of information and communication security, allowing students to consider long-term developments, trends, and challenges in their analysis.

Timetable: 

Date	Time	Session	Topic
13 October 2026	14:00 to 16:00	Lecture	Introduction
13 October 2026	16:00 to 18:00	Lecture	Authentication
20 October 2026	14:00 to 16:00	Lecture	Access Control
27 October 2026	14:00 to 16:00	Lecture	Cryptography I
27 October 2026	16:00 to 18:00	Lecture	Cryptography II
3 November 2026	14:00 to 16:00	Exercise
10 November 2026	14:00 to 16:00	Exercise
10 November 2026	16:00 to 18:00	Exercise
17 November 2026	14:00 to 16:00	GL	Guest Lecture by Michael Schmid
24 November 2026	14:00 to 16:00	Lecture	Electronic Signatures
24 November 2026	16:00 to 18:00	Lecture	Identity Management
1 December 2026	14:00 to 16:00	Lecture	Privacy Protection I
8 December 2026	14:00 to 16:00	Lecture	Privacy Protection II
8 December 2026	16:00 to 18:00	Lecture	Computer System Security
15 December 2026	14:00 to 16:00	Lecture	Network Security I
12 January 2027	14:00 to 16:00	Lecture	Network Security II
19 January 2027	14:00 to 16:00	Lecture	Exam Preparation and Wrap-Up Q&A
19 January 2027	16:00 to 18:00	Lecture	Recent Research
26 January 2027	14:00 to 16:00	Lecture	To be announced
2 February 2027	14:00 to 16:00	GL	Guest Lecture by Welderufael B. Tesfay
2 February 2027	16:00 to 18:00	GL	Guest Lecture by Jesus Luna Garcia
9 February 2027	14:00 to 16:00	Lecture	To be announced

 

Post exam review plan (TBA) 

*: updated

Exam Information

The language of the exam is English. The exam is also to be answered in English.

The exam has 90 points. 

Please bring a non-programmable calculator to the exam.

Old_Exams *

Lectures

Seminar: Human Behaviour with regard to Privacy, Cybersecurity, and AI

Basic Information

Type of Lecture:	Seminar
Course:	Master
Course Code	RAN1_W27
Hours/Week:	2
Maximum Participants	30
Credit Points:	6
Language:	English
Term:	Winter 2026/2027
Lecturers:	Prof. Dr. Kai Rannenberg  Ann-Kristin Lieberknecht
Email:	This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Learning Goals

• Students critically analyze how individuals and groups perceive, decide about, and act on privacy, cybersecurity, and AI-related risks, and identify the psychological, social, and contextual factors that shape these behaviors.
• They acquire theoretical and practical knowledge of behavioral models and learn to apply them to explain gaps between stated concern and actual protective behavior across different user groups.
• Through case studies and empirical methods, students acquire the skills to design for, evaluate, and critique interventions aimed at supporting informed, confident decision-making around privacy, security, and AI use.

Module Description

This seminar examines why people's privacy and security behavior often diverges from their stated concerns and intentions, and what determines whether protective action happens at all. Drawing on behavioral and psychological research, it explores factors such as self-efficacy, risk perception, trust, and social influence to understand this gap, and asks what it means for the design of technology, education, and policy meant to support better decisions.

The seminar is structured around core thematic areas including usable privacy and security, behavioral and psychological models of protective action, and the design and evaluation of interventions such as literacy training, nudges, peer-based support, and AI-based approaches.

Building on core knowledge in information systems and human-computer interaction, the course combines empirical research methods with analytical practice. Students engage with qualitative and/or quantitative approaches to studying behavior, conduct independent research, write academic papers, and present their findings in a seminar format. The seminar prepares students for master's theses and research-focused careers in usable security and privacy, human-centered AI, and behavioral research in technology contexts.

General: 

This module is limited to 30 participants. A two-stage registration procedure with different deadlines applies. You have to take part in both stages.

Stage 1: Application 
Deadline: 21 September 2026 – 05 October 2026

Module Application via QIS: My Functions > Lectures occupy/sign off (Instruction Module Application)

Stage 2: Exam Registration and Withdrawal 

Deadline: 08 October 2026 – 21 October 2026

Exam Registration and Withdrawal via QIS: My Functions > Administration of exams (Instruction Exam Registration)

An acceptance in the application procedure entitles students to register for the allocated module, but it does not replace an exam registration. Without an exam registration in stage 2, the allocated module claim of the module application in stage 1 expires!

Assignment of Available Capacities: 22 October 2026 – Detailed information will be published on this website in due time.

For exchange students

Module application as well as exam registration and withdrawal is not possible via QIS. Exchange students register or withdraw with a form within the exam registration and withdrawal deadline (not application deadline!). Forms and information are available at the Website of the Faculty's International Office.

Assessment

Successful completion of a portfolio and regular attendance; no written test in the central examination phase. The portfolio elements are completed during the course. The type and scope of the individual portfolio elements will be announced at the beginning of the course.

Information on exam dates and retake exams will be published on this website at the beginning of the semester.

Please read your student e-mails (@stud.uni-frankfurt.de) regularly and keep up to date with our News Ticker.

 Bias, Fairness and Privacy in AI Systems Winter 2025/2026 

Type of Lecture:	Seminar
Course:	Master
Hours/Week:	2
Credit Points:	6
Language:	English
Term:	Winter 2025/2026
Lecturers:	Prof. Dr. Kai Rannenberg
Email:	This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Module Description

This seminar is an in-depth exploration of the central concerns at the intersection of artificial intelligence, fairness, and privacy. It addresses the emergence of bias in AI systems—from data and model design to deployment contexts—and discusses how such biases can lead to discriminatory consequences and undermine principles of data protection.

The seminar is structured around fundamental thematic areas, including algorithmic transparency, explainability, legal and ethical AI accountability frameworks, and privacy-preserving machine learning. Students are encouraged to delve into real-case scenarios from domains like healthcare, finance, law enforcement, or education and reflect on the social implications of AI systems.

Building on the core knowledge in information systems and digital technologies, this seminar combines academic research methods. Students conduct independent research, write papers, and present their findings. The seminar prepares students for master's theses and research-focused careers in technology governance, digital ethics, and AI regulation.

 

Objectives

• Critically analyze current topics in fairness, transparency, and accountability of AI systems, with special attention to data protection and privacy.
• Acquire theoretical and practical knowledge about algorithmic bias, its reasons, and measures to counter it on technical, ethical, legal, and socio-economic levels.
• Through case studies of practical applications and policy frameworks (e.g., GDPR, EU AI Act), students acquire the skills to analyze and recommend interventions to assure ethical AI use.

 

Topics

• Bias in Algorithmic Decision-Making: Causes, Consequences, and Mitigation
• Explainability and Trust: The Role of XAI in Fair AI
• Regulation of Bias in AI

 

Learning Goals and Competencies

• Ability to understand and perform a systematic literature review 
• Basic understanding of privacy regulations and legal frameworks (e.g., GDPR, EU AI Act) in AI contexts
• Awareness of ethical issues in machine learning and their societal implications
• Demonstrate good writing and presentation skills
• Demonstrate good organisational skills and collaboration in working in groups

 

Stage 1: Application

Deadline: 22 September 2025 – 06 October 2025

Module Application via QIS: My Functions > Lectures occupy/sign off

 

Stage 2: Exam Registration and Withdrawal

Deadline: 09 October 2025 – 22 October 2025

Exam Registration and Withdrawal via QIS: My Functions > Administration of exams

An acceptance in the application procedure entitles students to register for the seminar but does not replace an exam registration. Without an exam registration in stage 2, the seminar claim from stage 1 expires.

Note: There is no assignment of available capacities for this seminar. Exam registration is only possible within the deadline and with prior acceptance.

 

For Exchange Students

Module application and exam registration are not possible via QIS. Exchange students must register or withdraw using a form within the exam registration and withdrawal deadline (not the application deadline). Forms are available on the Faculty’s International Office website.

 

Assessment

Successful completion requires a term paper with a presentation and regular attendance.

Specific topics will be introduced during the kick-off, and methodologies will be discussed before topic allocation. Students must carefully work through the methodology of their topic.

Exam dates and retake exams will be published on this website at the beginning of the semester.

Please check your student emails (@stud.uni-frankfurt.de) regularly and stay updated.

 

Literature (How to conduct a literature review):

• PRISMA-S
• PRISMA
• From Bias to Balance—A Systematic Literature Review on Gender Bias in AI (example)
• Document Analysis as a Qualitative Research Method
• Analyzing the Past to Prepare for the Future

 

Timetable

Date	What	Details
28.10.2025	Kick-off (RuW - RuW 2.202)	L01-AI Bias Data Privacy and Fairness
04.11.2025 (12:00 PM)	Submission of preferred topics (1-3)
11.11.2025	Distribution of topics
19.01.2026	Final Paper and Presentation submission
26.01.2026	Presentation day 1 (09:00 - 12:30)	SH 3.102
27.01.2026	Presentation day 2 (No Presentation)
28.01.2026	Presentation day 3 (09:00 - 13:00) + (14:30 - 18:00)	RuW 2.202

 

Presentation

• The presentation should be at most 15 minutes.
• You should present your key findings.
• The structure can follow the seminar paper (e.g., title slide, agenda, introduction, background, methodology, results, discussion, and conclusion)
• Everyone is requested to be present on both days from 09:00, regardless of their presentation timing allocated.
• Please bring your presentation slides with you (on your own device and/or on a USB as a backup).
• Please note that the presentation on the 28th will take place in room RuW 2.202.

 

Course Evaluation

 

Exam Information:

• In order to successfully pass this seminar, you need to write a paper (60%) and make a presentation (40%). Each partial requirement needs to be passed with a grade of 4.0 or better. 
• For the paper, the formal requirements of the chair apply (use this Template).

 

Mobile Business I - Technology, Markets, Platforms, and Business Models

 

Basic Information

Type of Lecture:	Lecture
Course:	Master
Hours/Week:	3
Credit Points:	6
Language:	English
Term:	Winter 2025/2026
Lecturers:	Prof. Dr. Kai Rannenberg
Email:	This email address is being protected from spambots. You need JavaScript enabled to view it.

 

 

Important information: 
For the Q&A Lecture, please send us your questions as bullet points via mail (This email address is being protected from spambots. You need JavaScript enabled to view it.) until 30.01.26 12:00 for us to prepare slides and answers. Questions can also be asked directly during the lecture.

 

 

Content of the Course