CMPSC 291A Intro to Differential Privacy: Theory, Algorithms and Applications

The graduate-level course covers the fundamentals of differential privacy (DP), as well as various applications of DP in statistical and machine learning applications.  Students will learn the fundamentals of DP and practice how to prove formal differential privacy guarantees. There will also be hands-on training on using “autodp” for state-of-the-art DP computation. The targeted audience of the course is graduate students in doing research in theory / algorithms, those doing applied research involving sensitive data (e.g., human subject data), as well as advanced undergraduate students with proper mathematical backgrounds.