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Tutorial 2: Defining ‘privacy’ for statistical databases

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DLAW01 - 'Data linkage and anonymisation: setting the agenda'

The tutorial will introduce differential privacy, a widely studied definition of privacy for statistical databases.

We will begin with the motivation for rigorous definitions of privacy in statistical databases, covering several examples of how seemingly aggregate, high-level statistics can leak information about individuals in a data set. We will then define differential privacy, illustrate the definition with several examples, and discuss its properties. The bulk of the tutorial will cover the principal techniques used for the design of differentially private algorithms. Time permitting, we will touch on applications of differential privacy to problems having no immediate connection to privacy, such as equilibrium selection in game theory and adaptive data analysis in statistics and machine learning.

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

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