Privacy Engineering and Privacy Enhancing Technology

Winter Semester 2026/2027


Title:	Privacy Engineering and Privacy Enhancing Technology
Type:	Lecture with Exercise
Token / Number / Module number:	PETS / - / 74094
Semester hours / Credits:	4 SCH / 6 CP
Lecturer:	Prof. Dr. Frank Kargl, Dr. Echo Meißner
Tutor:	Dennis Eisermann
General schedule:	Lecture: Tuesdays, 08:30 - 10:00, O27/341 (starting on 2026-10-13) Exercise: Mondays, 14:15 - 15:45, O27/341 (starting on 2026-10-19)
Learning platform:	https://moodle.uni-ulm.de/course/view.php?id=88565
Grade bonus:	By successfully participating in the exercises, a grade bonus can be earned, as specified in the module description. The exact conditions for the grade bonus will be announced in the exercise session.
Exam dates:	Oral exam by appointment

Description and general information

Integration of module into courses of studies:	Informatik, M.Sc., FSPO 2021 Praktische und Angewandte Informatik, Informatik, M.Sc., FSPO 2021 IT-Sicherheit, Informatik, M.Sc., FSPO 2021 Verteilte Systeme, Medieninformatik, M.Sc., FSPO 2021 Praktische und Angewandte Informatik, Medieninformatik, M.Sc., FSPO 2021 IT-Sicherheit, Medieninformatik, M.Sc., FSPO 2021 Verteilte Systeme, Software Engineering, M.Sc., FSPO 2021 Praktische und Angewandte Informatik, Software Engineering, M.Sc., FSPO 2021 IT-Sicherheit, Künstliche Intelligenz, M.Sc., FSPO 2021 Praktische und Angewandte Informatik, Informatik, B.Sc., FSPO 2022 Vertiefungsbereich, Medieninformatik, B.Sc., FSPO 2022 Vertiefungsbereich, Informatik, M.Sc., FSPO 2022 Praktische Informatik, Medieninformatik, M.Sc., FSPO 2022 Praktische Informatik, Software Engineering, M.Sc., FSPO 2022 Praktische Informatik, Künstliche Intelligenz, M.Sc., FSPO 2022 Praktische Informatik, Software Engineering, B.Sc., FSPO 2022 SE Wahlbereich
Modes of learning and teaching:	Privacy Engineering and Privacy Enhancing Technologies (Vorlesung) (3 SWS), Privacy Engineering and Privacy Enhancing Technologies (Übung) (1 SWS)
Module authority:	Prof. Dr. Frank Kargl
Lecturer:	Prof. Dr. Frank Kargl, Dr. Echo Meißner
Language:	english
Turn / Duration:	every winter term / 1
Requirements (contentual):	Security in IT Systems
Requirements (formal):
Basis for:	-
Learning objectives:	Participants will become familiar with modern privacy engineering. Starting from conducting Privacy Impact Assessments (PIAs) and a privacy risk analysis to designing privacy-friendly architectures all the way to application of privacy strategies and privacy enhancing technologies, the course covers the full lifecycle of privacy-friendly system design. Beyond mere theoretical knowledge, participants will practice their freshly acquired knowledge in many scenarios-based exercises.
Content:	The course briefly summarizes foundations of privacy and data protection and privacy analysis techniques like Privacy Impact Assessments and privacy risk analysis as discussed in more depth in module 71126 ("Grundlagen des Datenschutzes und der IT Sicherheit"). We then continue with privacy engineering methodologies like the one designed in the European PRIPARE project. This includes privacy strategies, privacy design patterns and many more. The second part of the lecture then focuses on technical privacy protection discussing different privacy strategies like minimization or hiding and privacy enhancing technologies like attribute-based credentials or group signatures.
Literature:	Selected literature and online resources.
Grading procedure:	The module examination consists of a graded written or oral examination, depending on the number of participants. If a specified academic work is achieved, a grade bonus is awarded in accordance with §17 (3a) of the General Examination Regulations at the immediately following examination. The examination grade is improved by one grade level, but not better than 1.0. An improvement from 5.0 to 4.0 is not possible. The examination form will be announced in good time before the examination is held - at least 4 weeks before the examination date.
Estimation of effort:	Presence teaching: 60h Self-study: 120h Total: 180h