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Data Privacy, 7.5 Credits

Swedish name: Datasekretess

This syllabus is valid: 2024-01-01 and until further notice

Course code: 5DV241

Credit points: 7.5

Education level: Second cycle

Main Field of Study and progress level: Computing Science: Second cycle, has only first-cycle course/s as entry requirements

Grading scale: Three-grade scale

Responsible department: Department of Computing Science

Established by: Faculty Board of Science and Technology, 2023-09-07


In the information age, information is continuously stored and transmitted. Most of this information is sensitive, and needs to be properly processed and stored to avoid undesirable disclosure. Data-driven models and data aggregates are not free from privacy risks.

Data privacy is the field that provides tools to avoid or control information leakage. This course aims to present privacy models  (computational definitions of privacy), techniques, and performance measures for privacy-preserving tools. During the course, the students will acquire knowledge about alternative perspectives of privacy as well as their implementation.

Expected learning outcomes

Knowledge and understanding
After completing the course, the student should be able to:

  • (FSR 1) understand privacy concepts and terminology
  • (FSR 2) understand different types of disclosure and privacy models

Competence and skills
After completing the course, the student should be able to:

  • (FSR 3) design, construct, and evaluate data protection mechanisms
  • (FSR 4) design, construct, and evaluate privacy-preserving data-driven computations
  • (FSR 5) comprehend scientific literature on data privacy

Judgement and approach
After completing the course, the student should be able to:

  • (FSR 6) have a critical mind in regards to privacy when designing and implementing a privacy-preserving solution

Required Knowledge

At least 90 ECTS, including 60 ECTS Computing Science, or at least 120 ECTS within a study programme. At least 15 ECTS programming; at least 30 ECTS mathematics, of which 7.5 ECTS statistics and 7.5 ECTS calculus. The course assumes the students have been introduced to machine learning, for example by a course on artificial intelligence. Proficiency in English equivalent to the level required for basic eligibility for higher studies.

Form of instruction

Instruction consists of lectures and computer-based assignments. In addition to scheduled activities, individual work with the material is also required.

Examination modes

The examination consists of written assignments and a written exam in halls. Grades given are Fail (U), Pass (G), and Pass with distinction (VG).

Adapted examination
The examiner can decide to deviate from the specified forms of examination. Individual adaptation of the examination shall be considered based on the needs of the student. The examination is adapted within the constraints of the expected learning outcomes. A student that needs adapted examination shall no later than 10 days before the examination request adaptation from the Department of Computing Science. The examiner makes a decision of adapted examination and the student is notified.

Other regulations

If the syllabus has expired or the course has been discontinued, a student who at some point registered for the course is guaranteed at least three examinations (including the regular examination) according to this syllabus for a maximum period of two years from the syllabus expiring or the course being discontinued.


Valid from: 2024 week 1

Torra Vicenç
Guide to data privacy : models, technologies, solutions
1st ed. 2022 : Cham : Springer International Publishing AG : [2022] : 313 Seiten :
ISBN: 9783031128363
Search the University Library catalogue