Privacy-preserving data analysis has a large literature that spans several disciplines. Many early attempts have proved problematic either in practice or on paper. A new approach, "differential privacy" -- a notion tailored to situations in which data are plentiful -- has provided a theoretically sound and powerful framework, and given rise to an explosion of research (see the NIPS 2014 tutorial on December 8). We will review the definition of differential privacy, describe some recent algorithmic advances, and conclude with a surprising application.