I can give a technical overview of the line of work characterizing differentially private PAC learnability in terms of combinatorial parameters from online learning (Littlestone dimension), and new notions of algorithmic stability/reproducibility in learning. I'd also plan to highlight open questions about the complexity of private learning and generalizations to other statistical/learning models that may be addressed using this perspective.