Theory of Differential Privacy

3 Credits.

This course is an introduction to differential privacy as a foundational framework for reasoning about privacy in data analysis. Students will develop a principled understanding of why privacy risks arise when privacy is not an explicit design objective and how differential privacy enables formal, provable guarantees. In this course, we will build on the algorithmic toolkit and statistical techniques for designing and analyzing differentially private methods, and study fundamental tradeoffs and lower bounds that characterize the limits of privacy. Required course background: students should be comfortable writing mathematical proofs involving algorithms, probability, and linear algebra.