Algorithmic and mathematical foundations of privacy protection 1000-2M19AOP
During this course we present fundamental algorithms (with related mathematical background) intended for providing privacy preserving when data revealing/processing. Our course is based on the newest results on differential privacy that is considered as an only standard for both theory and applications.
1.Introduction – what is differential privacy ? Different concepts of privacy. (1 lecture)
2. Probability theory – revision of basic facts (1 lecture)
3. Differential privacy; Laplace and Gauss mechanism (1-2 lecture)
4. Exponential mechanism, Composition theorems (1-2 lecture)
5. Privacy for releasing linear queries (2 lectures)
6. Privacy mechanism design (2-3 lectures)
7. Privacy and continual observation (2-3 lectures)
8. Lower bounds and computational complexity (1-2 lectures)
9. Privacy vs machine learning (2-3 lectures)
10. Differential privacy and cryptography (2-3 lectures)
Type of course
Learning outcomes
K_U01 Is able to construct mathematical reasonings.
Assessment criteria
Exam (60%) + 2 programming excercises (40%)
Bibliography
[1] Cynthia Dwork, Aaron Roth, The Algorithmic Foundations of Differential Privacy, Fundations and trends in TCS, 2014
[2] Attoh-Okine Nii O., Big Data and Differential Privacy, John Wiley & Sons Inc, 2017
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
- Bachelor's degree, first cycle programme, Computer Science
- Master's degree, second cycle programme, Computer Science
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: