Algorithmic and mathematical foundations of privacy protection
General data
Course ID: | 1000-2M19AOP |
Erasmus code / ISCED: |
11.3
|
Course title: | Algorithmic and mathematical foundations of privacy protection |
Name in Polish: | Algorytmiczne i matematyczne podstawy ochrony prywatności |
Organizational unit: | Faculty of Mathematics, Informatics, and Mechanics |
Course groups: |
(in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka Elective courses for Computer Science and Machine Learning |
ECTS credit allocation (and other scores): |
(not available)
|
Language: | Polish |
Type of course: | elective monographs |
Short description: |
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. |
Full description: |
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) |
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 |
Learning outcomes: |
K_U01 Is able to construct mathematical reasonings. |
Assessment methods and assessment criteria: |
Exam (60%) + 2 programming excercises (40%) |
Copyright by University of Warsaw.