Syllabus for Security and Privacy

Learning outcomes

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

• assess a data science scenario with respect to legal and ethical issues.
• analyse security and privacy considerations in data management life-cycle.
• explain algorithmic, technical and physical measures for secure and privacy-preserving data management.
• relate relevant machine learning aspects to the assessment, analysis and explanations of security and privacy issues and solutions in machine learning algorithms.

Content

This course introduces the data management life-cycle and its security and privacy challenges. The successful and reliable use of data science tools presents a range of security and privacy issues. Concepts include anonymity, privacy-preserving data processing, secure storage and data access as well as machine learning.

Instruction

Lectures, seminars, labs, assignments.

Assessment

The course is examined by oral and written examination (4 credits) spread out through the course and a written examination (3.5 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.