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