Privacy in the Land of Plenty

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. We will review the definition of differential
privacy, describe some recent algorithmic contributions, and conclude with a surprising application.

Biography:
Cynthia Dwork is a Distinguished Scientist at Microsoft Research in Silicon Valley. She is known for her research placing privacy-preserving
data analysis on a mathematically rigorous foundation, including the co-invention of Differential Privacy, a strong privacy guarantee
frequently permitting highly accurate data analysis. Dwork has also made contributions in cryptography and distributed computing, and is a
recipient of the Edsger W. Dijkstra Prize for her early work on the foundations of fault-tolerant systems.
Her contributions in cryptography include Non-Malleable Cryptography, the first lattice-based cryptosystem, which was also the first
public-key cryptosystem for which breaking a random instance is as hard as solving the hardest instance of the underlying mathematical problem
("worst-case/average-case equivalence"), and a technique for combating e-mail spam by requiring a proof of computational effort, also known as
Proof-of-work. This is the technology underlying hashcash and bitcoin. She was elected as a Fellow of the American Academy of Arts and Sciences in 2008, as a member of the National Academy of Engineering in 2008, and as a member of the National Academy of Sciences in 2014.