Data Privacy (Spring 2026)

Course Overview

How can we harness the power of data while protecting individual privacy? This course dives into one of the most critical challenges of our time by exploring privacy threats and cutting-edge solutions. Together, we’ll investigate privacy attacks, analyze their implications, and build a strong foundation in state-of-the-art techniques like differential privacy.

This course is designed to balance theory with practice. You’ll engage with real-world problems, develop practical skills, and explore how privacy-preserving methods are applied in diverse fields. Through projects, quizzes, and collaborative discussions, you’ll gain both technical expertise and a nuanced understanding of the ethical dimensions of data privacy.

What You’ll Gain

Who Should Enroll?

This course is ideal for students curious about the intersection of data science, ethics, and security. To succeed, you should have:

Why Take This Course?

Data privacy is not just a technical challenge—it’s a societal imperative. By the end of this course, you’ll be equipped to tackle complex privacy issues in academic research or industry settings. Whether your goal is to innovate in artificial intelligence or contribute to policy-making, this course will provide the tools and insights necessary to make a meaningful impact.

Course Info

Grading

Tentative Schedule

Week Dates Monday Wednesday
1 Jan 12 - Jan 16 No class (semester transition) Intro + course overview
2 Jan 19 - Jan 23 MLK Holiday (no class) Privacy attacks
3 Jan 26 - Jan 30 Privacy attacks in ML/LLMs Threat models + auditing (Lab 1)
4 Feb 2 - Feb 6 Differential privacy basics DP mechanisms (Laplace/Gaussian)
5 Feb 9 - Feb 13 Composition + privacy accounting Private learning (DP-SGD) (Lab 2)
6 Feb 16 - Feb 20 Advanced DP-SGD / Local DP No Class (Lab/Project Work)
7 Feb 23 - Feb 27 Motivation Presentations (1/2) Motivation Presentations (2/2) (Project Proposal Due)
8 Mar 2 - Mar 6 Spring recess Spring recess
9 Mar 9 - Mar 13 Cryptography: Secret Sharing + MPC Homomorphic Encryption (HE) basics (Lab 3)
10 Mar 16 - Mar 20 Secure Inference (MPC vs TEEs) Private Information Retrieval (PIR)
11 Mar 23 - Mar 27 Revisit: LLM privacy (memorization + extraction) Agent privacy: RAG/tool leakage (Lab 4)
12 Mar 30 - Apr 3 Midterm Progress Presentations (1/4) Midterm Progress Presentations (2/4)
13 Apr 6 - Apr 10 Midterm Progress Presentations (3/4) Midterm Progress Presentations (4/4)
14 Apr 13 - Apr 17 Guest Lecture / Advanced Topic No Class (Project Work)
15 Apr 20 - Apr 24 Project work / Office Hours Project work / Office Hours
16 Apr 27 - May 1 Poster Session (Project Report Due) No class (courses end Apr 28)

More resources

Courses

Core DP & privacy

Other flavors (theory, systems, fairness, ML)

Books

Cryptography & MPC

Privacy-enhancing technologies (DP, anonymization)