CS 860 - Algorithms for Private Data Analysis - Fall 2020

Course Description

This course is on algorithms for differentially private analysis of data. As necessitated by the nature of differential privacy, this course will be theoretically and mathematically based. References to practice will be provided as relevant, especially towards the end of the course. Prerequisites include an undergraduate understanding of algorithms, comfort with probability, and mathematical maturity.


This course has a Piazza (for discussion), Google Groups email list (for announcements), and a Microsoft Teams (for live sessions and/or office hours). Please contact the instructor if you are not in all of these.

Topics (Tentative)

Here is a list of topics which we may yet cover in this course.

Weekly Schedule

There will be two lectures posted each week, tentatively on Tuesday and Thursday.
There will be a quiz posted each week, simultaneously with the second lecture. It will be due by the first lecture of the following week (no earlier than Tuesday).


Lecture Number Release Date Video Link Lecture Notes Written Notes References and Readings
1 9/8/2020 Part 1
Part 2
PDF PDF Required: Recommended: Optional:
2 9/10/2020 Part 1
Part 2
Part 3
PDF PDF Highly Recommended: Recommended: Optional:
3 9/15/2020 Part 1
Part 2
PDF PDF Recommended: Optional:
4 9/17/2020 Part 1
Part 2
PDF PDF Recommended: Optional:
5 9/23/2020 Part 1
Part 2
PDF PDF Recommended: Optional:
6 9/24/2020 Part 1
Part 2
PDF PDF Recommended: Optional:


Books and References

Don't buy a textbook for this course. But we'll be going off the following resources, and others. Also check out the Resources page on DifferentialPrivacy.org.


Grades for this course will be determined with the following breakdown: