Data Privacy (Spring 2025)

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.

Instructor: Tianhao Wang
Co-Instructor: Xiaochen Li
Location: Rice 340
Time: Monday and Wednesday 12:30pm - 1:45pm
TA: Yan Pang
Discussion: Piazza from Canvas
Office Hour
- Tianhao (Rice 506): Monday and Wednesday 1:45pm - 2:45pm
- Xiaochen (Rice 542): Thursday and Friday 1pm - 2pm
- Yan (Rice 542): Tuesday 2 - 3pm
- (you can always email to request additional meeting or zoom meeting)
Policy

Grading:

Paper presentation (30%): During the semester, each student will present two papers, each for 15 minutes, or one paper for 30 minutes. If the slides are public or there are videos available online, it is okay to reuse the slides, but make sure you make a high-quality presentation. Students need to email me about the paper title to get an approval first. Click here to register.
Cyber Chats (10%): Each student submits two video recordings each of 5min. The video can investigates a related paper or social event.
Quiz (20%): There will be two in-class quizes.
Project (40%): Click here to register your project with a tentative title. Students are encouraged utilize office hours to get feedbacks on the project. Students need to submit project proposals (10%), present a poster (15%), and submit your final report (15%).
Grading Scale: We use the UVA default Grading Basis

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

More recourses

Privacy in Statistics and Machine Learning (video) Spring 2023 by Adam Smith (BU) and Jonathan Ullman (NEU)
Algorithms for Private Data Analysis (video) Fall 2022 by Gautam Kamath (Waterloo)
Applied Privacy for Data Science Spring 2022 by James Honaker, Wanrong Zhang, and Salil Vadhan (Harvard)
Introduction to Differential Privacy: Theory, Algorithms and Applications (video) Fall 2024 by Yuxiang Wang (UCSD)
Algorithms for Private Data Analysis Fall 2020 by Aleksandar Nikolov (UofT)

Books

More courses on other flavors

Theory: The Algorithmic Foundations of Adaptive Data Analysis Fall 2017 by Aaron Roth (Penn) and Adam Smith (BU)
System: Private Systems Spring 2020 by Roxana Geambasu (Columbia)
Mechanism design: Differential Privacy in Game Theory and Mechanism Design Spring 2014 by Aaron Roth (Penn)
Fairness: Privacy and Fairness in Data Science Fall 2021 by Ashwin Machanavajjhala (Duke)
ML: Privacy Preserving Machine Learning Spring 2022 by Aurélien Bellet (Inria)