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Schedule

Deadlines

  • Homework 1: 9/26
  • Homework 2: 10/17

Lecture Schedule (Tentative)

Week Lecture Date Topic Readings Slides
Notes
Week 1 Lecture 1 (8/31) Course Overview Slides
Week 1 Lecture 2 (9/1) Reconstruction Attacks (Part 1) Reading Slides
Note
Week 1 (9/3) No Recitation
Week 2 (9/6) Labor Day: No Class
Week 2 Lecture 3 (9/8) Reconstruction Attacks (Part 2) Reading Slides
Note
Week 2 Recitation 1 (9/10) Probability Review (HW 0) HW 0
Definitions and Basic Techniques
Week 3 Lecture 4 (9/13) Definition of Differential Privacy
Randomized Response
Slides
Note
Week 3 Lecture 5 (9/15) Laplace Mech. (Part 1) Reading Slides
Note
Week 3 Recitation 2 (9/17) DP Review
Week 4 Lecture 6 (9/20) Laplace Mech. (Part 2) Slides
Note
Week 4 Lecture 7 (9/22) Properties of DP (Part 1) Slides
Note
Week 4 Recitation 3 (9/24) DP/Laplace Review
Week 5 Lecture 8 (9/27) Properties of DP (Part 2) Slides
Note
Week 5 Lecture 9 (9/29) Selection problem (Part 1)
Exponential Mech.
Slides
Note
Week 5 Recitation 4 (10/1) Review/HW1
Week 6 Lecture 10 (10/4) Selection problem (Part 2) Slides
Note
Week 6 Lecture 11 (10/6) Approximate DP
Gaussian Mech. (Part 1)
Slides
Note
Week 6 Recitation 5 (10/8) Review (in-person)
Week 7 Lecture 12 (10/11) Approximate DP
Gaussian Mech. (Part 2)
Slides
Note
Week 7 Lecture 13 (10/13) No Class: Optional guest lecture on zoom
Week 7 Recitation 6 (10/15) Review
Private Machine Learning
Week 8 Lecture 14 (10/18) Basic Machine Learning Slides
Note
Week 8 Lecture 15 (10/20) Basic Machine Learning
Gradient descent
Week 9 Lecture 16 (10/25) Private Gradient Descent
Week 9 Lecture 17 (10/27) DP Synthetic Data (Part 1)
Week 10 Lecture 18 (11/1) DP Synthetic Data (Part 2)
Week 10 Lecture 19 (11/3) Buffer
Fairness in Machine Learning
Week 11 Lecture 20 (11/8) Fair ML (Part 1)
Week 11 Lecture 21 (11/10) Fair ML (Part 2)
Week 12 Lecture 22 (11/15) Fairness and Causal Inference
Cryptographic Tools
Week 12 Lecture 23 (11/17) TBA
Week 13 Lecture 24 (11/22) TBA
Week 13 (11/24) Thanksgiving: No Class
Week 14 Lecture 25 (11/29) TBA
Week 14 Lecture 26 (12/1) Review (Last Lecture)

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