Statistical Asymptotics with Differential Privacy

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• Daniel Kifer (Pennsylvania State University)
• Friday 09 December 2016, 11:15-12:00
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

DLAW03 - New developments in data privacy

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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