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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 Definitoin 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
Week 4 Lecture 8 DP and Mechanism Design Slides
Private Synthetic Data
Week 5 Lecture 9 Private GAN
(Online) Private Multiplicative Weights
Private (Non)-Convex Optimization
Week 5 Lecture 10 (Strong) Convexity, smoothness
Output/Objective Perturbation
Week 6 Lecture 11 Private Gradient Descent (Part 1)
Week 6 Lecture 12 Private Gradient Descent (Part 2)
Week 7 Lecture 13 Private Deep Learning (Part 1)
Week 7 Lecture 14 Private Deep Learning (Part 2)
Practical Deployments of DP
Week 8 Lecture 15 Local Differential Privacy
Week 8 Lecture 16 Shuffling
Week 9 Lecture 17 US Census Deployment 2020
Week 9 Lecture 18 Buffer
Connections and Applications
Week 10 Lecture 19 Online Learning
Follow-the-perturbed-leader via DP
Week 10 Lecture 20 Adaptive Data Analysis
Algorithmic Stability

Acknowledgement: Some of course materials are based on those developed by Gautam Kamath, Jonathan Ullman, and Adam Smith.

Deadlines

TBA