Differential Privacy as a Learning Problem

This talk is about privacy-preserving analysis of sensitive data. We are interested in algorithms that satisfy the strong privacy guarantee known as differential privacy.
We show how to interpret differentially private data analysis as a learning problem. We use this connection to give several powerful algorithms for this task.
We conclude with the main algorithmic challenges that remain open.

I will assume no knowledge of differential privacy.