Course Identification

While there are no formal prerequisites, an undergraduate-level familiarity with algorithm design and analysis and probability is expected. An undergraduate-level knowledge of complexity theory and machine learning will also be helpful.

This course provides a foundational perspective on individual privacy in the context of statistical data analysis and machine learning. The focus will be on differential privacy, a rigorous mathematical formulation of individual privacy. We will study privacy concerns and attacks, the framework of differential privacy, state-of-the-art differentially private algorithms for data analysis and machine learning, and (time permitting) connections to adaptive data analysis and fair classification.

Upon successful completion of this course students should be able to:

1. Demonstrate understanding of basic privacy attacks and concerns, such as re-identification, reconstruction and differencing attacks, composition.
2. Demonstrate understanding of the definition and guarantees provided by (several variants of) differential privacy.
3. Demonstrate understanding of basic and advanced algorithms for privacy-preserving data analysis, and a "toolbox" for differentially private algorithm design.

The Algorithmic Foundations of Differential Privacy, Dwork and Roth