Data Privacy

Education facts

Code: DIT117

This course explores the growing need for privacy-preserving techniques in data sharing, with a particular focus on Differential Privacy. Students will learn about principles behind laws like the GDPR, as well as traditional techniques for data anonymization. The course delves into more advanced privacy-preserving mechanisms like Differential Privacy and its applications in real-world systems, and synthetic data generation. Through a mix of theoretical lectures and practical assignments, students will develop skills in identifying privacy risks, implementing privacy mechanisms, and analyzing the trade-offs between data utility and privacy. The course is suitable for those looking to work in areas related to data privacy, security, and policy.

Entry requirements

90 hp points in courses in Computer Science and/or Mathematics or equivalent. Among those courses, the student should have: - 5\.0 credits in calculus - 7\.5 creditson Statistical Methods for Data Science (DIT863) or equivalent.  - 7\.5 credits on a course in functional programming (DIT143) or programming with Python (MVG301), or equivalent

Education offerings

Each offering has its own dates and conditions. Closed offerings are retained as history and do not mean that a new application is open.

• Data Privacy

  University of Gothenburg

  Start date: 2 November 2026

  End date: 17 January 2027

  Pace of study: 50 %