Entry requirements

120 credits including a second course in programming. Participation in a course in machine learning. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

• analyze security and privacy considerations in a typical data life cycle.
• explain and compare algorithmic, technical, and physical measures for secure and privacy-preserving data science.
• discuss the trade-offs and limits of secure and privacy-preserving data science.
• reflect on the legal and ethical issues of a data science scenario.

Content

The course introduces security and privacy challenges in the data life cycle. Concepts of threat models, secure computation, privacy-preserving data processing, as well as security issues related to machine learning are introduced.

Instruction

Lectures, seminars, labs, assignments.

Assessment

The course is examined by oral and written presentation (3 credits) spread out through the course and a written report (2 credits).

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

The unit cannot be included in a degree with Security and Privacy (1DT098)