Computer Science 208: Applied Privacy for Data Science

The risks to privacy when making human subjects data available for research and how to protect against these risks using the formal framework of differential privacy. Methods for attacking statistical data releases, the mathematics of and software implementations of differential privacy, deployed solutions in industry and government. Assignments will include implementation and experimentation on data science tasks.

Spring 2019 (James Honaker and Salil Vadhan)

Spring 2022 (James Honaker, Salil Vadhan, Wanrong Zhang)