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Statistical Asymptotics with Differential Privacy

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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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