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.
Cynthia Dwork (Microsoft Research)
Cynthia Dwork, Distinguished Scientist at Microsoft Research, is renowned for placing privacy-preserving data analysis on a mathematically rigorous foundation. A cornerstone of this work is differential privacy, a strong privacy guarantee frequently permitting highly accurate data analysis. Dr. Dwork has also made seminal contributions in cryptography and distributed computing, and is a recipient of the Edsger W. Dijkstra Prize, recognizing some of her earliest work establishing the pillars on which every fault-tolerant system has been built for decades. She is a member of the National Academy of Sciences and the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences.
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Cynthia Dwork · Vitaly Feldman · Moritz Hardt · Toni Pitassi · Omer Reingold · Aaron Roth