Privacy in Statistics and Machine Learning
Lecture videos (from 2021) can be found in this Google Drive folder.Spring 2023
| Date | Topics | Deliverables |
|---|---|---|
| Thu 1/19 |
Lecture 1: Course Overview Video: Watch these two MinutePhysics videos (1, 2). They introduce some of the course topics in the context of the US Census. Lecture Slides: pdf In-class Exercises: pdf (We did not get to these but they are posted for reference.) |
|
|
Tue 1/24 |
Lecture 2:
Reconstruction Attacks
I
Video: part 1, part 2 Lecture Notes: pdf that goes with videos (up through 2.3). Additional optional reading on reconstruction: Confidence-Ranked Reconstruction of Census Microdata from Published Statistics In-class Exercises: pdf |
|
| Thu 1/26 | Lecture 3:
Reconstruction Attacks
II Lecture Notes: pdf (2.4 onward) Video: part 1, part 2 In-class Exercises: pdf |
|
| Tue 1/31 |
Lecture 4: Differential Privacy Fundamentals I Lecture Notes: pdf Video: part 1, part 2, part 3, part 4 In-class Exercises: pdf |
|
| Thu 2/2 |
Lecture 5: Differential Privacy Fundamentals II Lecture Notes: pdf Video: part 1, part 2, part 3 In-class Exercises: pdf |
|
| Tue 2/7 |
Lecture 6: Exponential Mechanism and Report Noisy Max Lecture Notes: (To be posted soon. For now, see last year's notes: pdf) Video: part 1, part 2, part 3, part 4, In-class Exercises: pdf |
|
| Thu 2/9 |
Lecture 7: Recap and
Project
Discussion In-class Exercises: pdf Project Info: pdf |
Homework 1 out (due 2/26): pdf, tex |
| Tue 2/14 |
Lecture 8:
The Binary Tree Mechanism Lecture Notes: pdf Video: part 1, part 2 In-class Exercises: pdf |
|
| Thu 2/16 |
Lecture 9: Approximate DP Lecture Notes: pdf Lecture Slides: pdf Video: part 1, part 2 In-class Exercises: None (traditional lecture) |
|
| Tue 2/21 | No class (Monday schedule) | |
| Thu 2/23 |
Lecture 10: Advanced Composition Lecture Notes: pdf Video: part 1, part 2 In-class Exercises: pdf |
|
| Tue 2/28 |
Lecture
11: Private Empirical Risk Minimization Lecture Notes: pdf Video: part 1, part 2 In-class Exercises: N/A (traditional lecture). |
|
| Thu 3/2 |
Lecture
12: Recap (We discussed exercises from the previous two lectures.) |
|
| 3/6-3/10 | Spring break | |
| Tue 3/14 |
Lecture
13: Private Gradient Descent Lecture Notes: pdf Video: part 1, part 2 In-class Exercises: pdf |
|
| Thu 3/16 |
Lecture 14: DP-FTRL
We discussed how a few different ideas from the class so far come together in he approach of Kairouz, McMahan, Song, Thakkar, Thakurta, Xu (NeurIPS 2021) (We sketched the privacy analysis and proof of Theorem 5.1) |
|
| Tue 3/21 |
Lecture
15: Factorization Mechanisms Lecture Notes: pdf (up to 2.3) Video: part 1, part 2 In-class Exercises: pdf |
|
| Thu 3/23 |
Lecture
16: The Projection Mechanism Lecture Notes: pdf (2.3 onward) Video: part 1 In-class Exercises: pdf |
|
| Tue 3/28 |
Lecture 17: Online
Learning and Multiplicative Weights Lecture Notes: pdf (Sections 1 to 3.1) Video: part 1, part 2, part 3, part 4 In-class Exercises: pdf |
|
| Thu 3/30 |
Lecture 18: Synthetic Data
Generation and Online Learning Lecture Notes (hand-written, from lecture): pdf Video: part 1, part 2, part 3, part 4, part 5, part 6. In-class Exercises: pdf Note: For more coverage of this material, see the videos and notes for Lectures 17 and 18 from 2021 (on two-player games and synthetic data generation) |
|
| Tue 4/4 |
Lecture 19:
Differential Privacy for
Network Data
Guest lecture by Sofya Raskhodnikova (Based on a paper shared on Piazza with class.) |
|
| Thu 4/6 |
Lecture 20: Lower
Bounds from Reconstruction and
Membership Inference Attacks
Lecture Notes: pdf |
Project progress reports due Friday, 4/7 |
| Tue 4/11 |
Lecture 21: A Simple
Analysis of Robust Membership
Inference Attacks
... in which we prove the Fingerprinting Lemma without using calculus. (No lecture notes yet.) |
|
| Thu 4/13 |
Lecture 22: Private Statistical Inference Lecture slides: pdf |
|
| Tue 4/18 |
Lecture 23: Private
PAC Learning (Guest lecturer: Satchit Sivakumar) Lecture notes: pdf |
|
| Thu 4/20 |
Lecture 24: OpenDP Tutorial (Guest lecturer: Michael Shoemate) Further reading: Documentation, Repository. |
|
| Tue 4/25 |
Lecture 25: Adaptive
Data Analysis Lecture slides: pdf |
|
| Thu 4/27 |
Lecture
26: The Local
Model Lecture slides: pdf |
|
| Tue 5/2 | Recap Exercises In-class exercises: pdf | Draft project report due 5/3 |
| Tue 5/2, 11:00 AM - 1:00 PM | Project Presentations | Revised project report due 5/10 |