Schedule
Lecture Schedule (Tentative)
| Week | Lecture | Topic | Readings | Slides Notes |
|---|---|---|---|---|
| Week 1 | Lecture 1 | Course Overview | Slides | |
| Definitions and Basic Techniques | ||||
| Week 1 | Lecture 2 | Reconstruction Attacks (Part 1) | Reading | Slides Notes |
| Week 2 | Lecture 3 | Reconstruction Attacks (Part 2) | Reading | Slides Notes |
| Week 2 | Lecture 4 | Definition of Differential Privacy Randomized Response; Laplace Mechanism |
Reading/Video | Slides Notes |
| Week 3 | Lecture 5 | Properties of Differential Privacy Composition; Post-Processing; Group Privacy |
Slides Notes |
|
| Week 3 | Lecture 6 | Selection problem (part 1) Exponential Mech. |
Slides Notes |
|
| Week 4 | Lecture 7 | Selection problem (part 2) Report noisy max |
Slides Notes |
|
| Week 4 | Lecture 8 | DP and Mechanism Design | Slides Notes |
|
| Week 5 | Lecture 9 | Binary Tree Mechanism | Slides Notes |
|
| Week 5 | Lecture 10 | Approximate Differential Privacy Gaussian Mechanism |
Slides Notes |
|
| Week 6 | Lecture 11 | Advanced Composition | Slides Notes |
|
| Private (Non)-Convex Optimization | ||||
| Week 6 | Lecture 12 | Private ERM | Slides Notes |
|
| Week 7 | Lecture 13 | Private Gradient Descent | Slides Notes |
|
| Week 7 | Lecture 14 | Private Deep Learning | Slides 1 Slides 2 |
|
| Query Release and Synthetic Data | ||||
| Week 8 | Lecture 15 | Factorization Mechanism | Slides Notes |
|
| Week 8 | Lecture 16 | Projection Mechanism Online Learning |
Slides Notes |
|
| Week 9 | Lecture 17 | (Private) Multiplicative Weights MWEM |
Slides | |
| Week 9 | Lecture 18 | Zero-Sum Game | Slides | |
| Week 10 | April 5th No Class | |||
| Week 10 | Lecture 19 | Synthetic Data | Slides | |
| Local Model | ||||
| Week 11 | Lecture 20 | Local Differential Privacy (Part 1) | Slides | |
| Week 11 | Lecture 21 | Local Differential Privacy (Part 2) | Slides | |
| Connections and Applications | ||||
| Week 12 | Lecture 22 | Adaptive Data Analysis (Part 1) | Slides | |
| Week 12 | Lecture 23 | Adaptive Data Analysis (Part 2) | Slides | |
| Week 13 | Lecture 24 | Adaptive Data Analysis (Part 3) | Slides | |
| Week 13 | Lecture 25 | No Class | ||
| Week 14 | Lecture 26 | Project Presentations (May 3) | ||
| Week 14 | Lecture 27 | Project Presentations (May 5) |
Acknowledgement: Some of course materials are based on those developed by Gautam Kamath, Jonathan Ullman, Adam Smith, and Aaron Roth.
Deadlines
TBA