Data Privacy (Spring 2025)

Note: Under construction. This is a direct copy of the Spring 2022 offering. Everything may change dramatically. How can we learn from data while protecting individuals’ privacy? This course addresses that question, starting with privacy attacks and progressing to rigorous, state-of-the-art solutions, such as differential privacy. We will cover both theoretical foundations and practical challenges involved in real-world applications. Prerequisites for this course include a basic understanding of machine learning (e.g., knowledge of how large language models work), proficiency in coding (e.g., Python), and familiarity with reading and writing algorithmic proofs that involve probability.

Grading:

Schedule (tentative, subject to change), we will meet in-person

Week Dates Monday Wednesday
1 Jan 13 - Jan 17 Intro Privacy Attacks
2 Jan 20 - Jan 24 Privacy Attacks Privacy Attacks
3 Jan 27 - Jan 31 Privacy Attacks Privacy Attacks
4 Feb 3 - Feb 7 Privacy Enhancing Technologies Privacy Enhancing Technologies (HW 1 Due)
5 Feb 10 - Feb 14 Privacy Enhancing Technologies Privacy Enhancing Technologies
6 Feb 17 - Feb 21 Privacy Enhancing Technologies Privacy Enhancing Technologies
7 Feb 24 - Feb 28 Student-led Presentation Student-led Presentation (HW 2 Due)
8 Mar 3 - Mar 7 Spring break Spring break
9 Mar 10 - Mar 14 Student-led Presentation Student-led Presentation
10 Mar 17 - Mar 21 Student-led Presentation Student-led Presentation (Cyber Chat Due)
11 Mar 24 - Mar 28 Cyber Chat Discussion Cyber Chat Discussion
12 Mar 31 - Apr 4 Student-led Presentation Student-led Presentation (Project Proposal Due)
13 Apr 7 - Apr 11 Student-led Presentation Student-led Presentation
14 Apr 14 - Apr 18 Student-led Presentation Student-led Presentation
15 Apr 21 - Apr 25 Work on Poster Work on Final Project (Final Project Poster Due)
16 Apr 28 - Apr 30 Poster Session No Class (Final Project Report Due)

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