A short tutorial on differential privacy

Speaker: Borja Balle (Amazon Research, UK)

Date: 26 January 2018

Time: 14:00 – 16:30

Location: The Alan Turing Institute

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Differential privacy is a robust mathematical framework for designing privacy-preserving computations on sensitive data. In this tutorial we will cover the key definitions and intuitions behind differential privacy and introduce the core building blocks used by most differentially private mechanisms.

The first half of the tutorial will introduce the basic ideas and provide a brief survey of some of their applications in privacy-preserving machine learning. In the second half of the tutorial we will present several variants of the original definition of differential privacy, and discuss the roles each of these definitions plays in practical applications.

This is the first one of a series of talks in the context of the interest group on Privacy-Preserving Data Analysis


Borja Balle is currently a Machine Learning Scientist at Amazon Research Cambridge. Before joining Amazon, Borja was a lecturer at Lancaster University (2015-2017) and a postdoctoral fellow at McGill University (2013-2015).

His main research interest is in privacy-preserving machine learning, including the use of differential privacy and multi-party computation in distributed learning problems, and the foundations of privacy-aware data science.

More info at https://borjaballe.github.io