CPSC 538L 201 2021W

Machine learning ecosystems run on vast amounts of personal information which are digested into models used for understanding and prediction. However, these ML models have been shown to leak information about users. Differential Privacy enables privacy-preserving statistical analyses on sensitive datasets with provable privacy guarantees. As such, it is seeing increasing interest from both academia and industry. In this course we will explore Differential Privacy theory, and its application to machine learning, from individual models to end-to-end applications.

The learning objectives for this class are to:

• Understand the challenges and importance of privacy in ML.
• Learn the basics of Differential Privacy (DP) theory.
• Get a deeper understanding of some advanced topics in DP though a focus on three broad topics: privacy attacks, DP deep learning, DP workloads.
• Have the necessary tools, and a first experience, to conducting DP research.

This is a seminar course with a majority of the lecture time devoted to student-led paper presentations and discussions, although there will be some lectures.