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Statistical Asymptotics with Differential Privacy

Presented by: 
Daniel Kifer Pennsylvania State University
Date: 
Friday 9th December 2016 - 11:15 to 12:00
Venue: 
INI Seminar Room 1
Abstract: 
Differential privacy introduces non-ignorable noise into synthetic data and query answers. A proper statistical analysis must account for both the sampling noise in the data and the additional privacy noise. In order to accomplish this, it is often necessary to modify the asymptotic theory of statistical estimators. In this talk, we will present a formal approach to this problem, with applications to confidence intervals and hypothesis tests.

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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons