Clément Lalanne, PhD at Dante and UMPA, will give a series of mini lectures on Differential Privacy.
Differential Privacy is a tractable property that aims at protecting the privacy of the individuals composing a dataset.
- December 9th 2020 at 3:30pm : Introductory lecture on Differential Privacy. On this first lecture we will go through the basic definitions and properties of this concept while explaining why it is appealing for real world applications. We will also cover some modest examples. For the next lectures, Clément plans to cover some general techniques to turn an existing algorithm into a differentially private one and to present some advanced techniques in order to track the privacy loss through composition of algorithms.
- December 16th 2020 at 3:45pm : We will focus on the techniques that add noise to the output of an algorithm in order to enhance privacy.
- January 13th 2021 at 9:00am : We will study the graphical representation of differential privacy in an hypothesis testing setup and use it to deduce some properties including the advanced sharp composition theorem for Differential Privacy.
After the lectures, the slides and a detailed pdf will be available at https://clemlal.github.io/privacy.