Schedule of Lectures
This year the course will be Quercus (needs U of T access). For those outside U of T who are interested, I will be posting updated reading materials below.
Check out this short list of useful probability theory facts.
[V] denotes Vadhan's notes, and [DR] denotes Dwork and Roth's monograph.
- Attacks: reconstruction and tracing attacks against privacy;
Reading: Notes on Attacks (updated). Also check this survey. Slides. - Differential Privacy: definition; equivalent formulations; Randomized Response;
Reading: Section 1 of [V], Chapter 2 of [DR]. Slides. - Properties of DP: composition; group privacy;
Reading: Section 2 of [V], Sections 3.1-3.2 and beginning of 3.5. Slides. - Laplace and Gaussian noise mechanisms.
Reading: Sections 3.3 and Appendix A of [DR]. Slides for Laplace. Slides for Gaussian. - Exponential mechanism: Private PAC learning
Reading: Sections 3.4, 11.1 of [DR], Section 8.1. of [V]. Slides. - Empirical Risk Minimization: Private Gradient Descent;
Reading:Lecture notes. Slides. - Privacy and Adaptive Data Analysis: why private data analysis on the sample generalizes to the population;
Reading: Lecture notes. Slides. - Learning the Database: the multiplicative weight update algorithm;
Reading: Sections 4.2. of [DR], and Lecture notes (old, wait for an updated version). Slides. - The Projection Mechanism;
Reading: Section 12.4. of [DR], Sections 7.3. of [V], and Lecture notes. - Class Presentations.