Data Security and Privacy
Course, Master's level, 1DT114
Spring 2025 Spring 2025, Uppsala, 33%, On-campus, English
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 24 March 2025–8 June 2025
- Language of instruction
- English
- 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.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
-
If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 12,083
- Total tuition fee: SEK 12,083
- Application deadline
- 15 October 2024
- Application code
- UU-61216
Admitted or on the waiting list?
- Registration period
- 10 March 2025–31 March 2025
- Information on registration from the department
Spring 2025 Spring 2025, Uppsala, 33%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 33%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 24 March 2025–8 June 2025
- Language of instruction
- English
- 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.
Admitted or on the waiting list?
- Registration period
- 10 March 2025–31 March 2025
- Information on registration from the department
About the course
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.
On completion of the course, you 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.
Reading list
No reading list found.