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FDD3344 Privacy- Enhancing Technologies 7.5 credits

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Application

For course offering

Autumn 2023 Start 30 Oct 2023 programme students

Application code

51385

Headings with content from the Course syllabus FDD3344 (Spring 2019–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

  • Legal context for privacy in Europe
  • Fundamental privacy terminology and concepts
  • A range of privacy-enhancing technologies (PETs)

Intended learning outcomes

The students should be able to:

  • recognize threats to privacy
  • explain the basic privacy terminology and concepts and use them correctly
  • find and apply documentation of privacy-related problems and technologies
  • get an overview of existing privacy-enhancing technologies  (PETs)
  • analyze system PET descriptions in terms of their privacy protection and how they work
  • identify vulnerabilities of system descriptions and predict their corresponding threats
  • select counter-measures to identified threats and argue their effectiveness
  • compare counter-measures and evaluate their side-effects
  • present and explain their reasoning to others

such that the students can:

  • reason about privacy in general and PETs in particular and
  • incorporate existing PETs into their research or start developing new ones.

Literature and preparations

Specific prerequisites

This course is for PhD students in Computer Science or related subjects.

Recommended prerequisites

No information inserted

Equipment

No information inserted

Literature

The reading list will be available on the course website and will be amended as the course proceeds.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F

Examination

  • EXA1 - Examination, 7.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade

The grading is pass/fail. To pass the course, the students successfully complete the following tasks.

Do assigned reading

Select a topic

Suggest a relevant reading list for the other participants

Present the selected topic

Lead a discussion on the selected topic

Hand in a written assignment

Participate in at least 80% of the meetings, preferably in person

Missed meetings can be made up by a written report on the meeting topics.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted

Supplementary information

This course has been developed within SWITS, which is a network for security researchers in Sweden, mainly PhD students. Together with Simone Fischer-Hübner at Karlstad Unversity, we offer this PhD course on Privacy-Enhancing Technologies that can be attended by SWITS PhD students also from other locations in Sweden.

Examiners are:
Sonja Buchegger at KTH and Simone Fischer-Hübner at KAU.

Postgraduate course

Postgraduate courses at EECS/Theoretical Computer Science