Algorithmic and mathematical foundations of privacy protection

General data

Course ID:	1000-2M19AOP
Erasmus code / ISCED:	11.3 The subject classification code consists of three to five digits, where the first three represent the classification of the discipline according to the Discipline code list applicable to the Socrates/Erasmus program, the fourth (usually 0) - possible further specification of discipline information, the fifth - the degree of subject determined based on the year of study for which the subject is intended. / (0612) Database and network design and administration The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
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) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
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%)

This course is not currently offered.

Course descriptions are protected by copyright.
Copyright by University of Warsaw.