Microsoft Security Data Science Colloquium: Inference Privacy in Theory and Practice

Here are the slides for my talk at the Microsoft Security Data Science Colloquium:
When Models Learn Too Much: Inference Privacy in Theory and Practice [PDF]

The talk is mostly about Bargav Jayaraman’s work (with Katherine Knipmeyer, Lingxiao Wang, and Quanquan Gu) on evaluating privacy:

• Merlin, Morgan, and the Importance of Thresholds and Priors
• Evaluating Differentially Private Machine Learning in Practice