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Thomas Berrett (CREST) – “Local Differential Privacy”

February 19, 2:00 pm - 3:00 pm

The Statistics-Econometrics-Machine Learning Seminar.

Time: 14:00 pm – 15:00 pm
Date: 19th of February 2020
Place: Room 3001.

Thomas BERRETT (CREST) – “Local Differential Privacy”

Abstract : In recent years, it has become clear that in certain studies there is a need to preserve the privacy of the individuals whose data is collected. As a way of formalising the problem, the framework of differential privacy has prevailed as a natural solution. The privacy of the individuals is protected by randomising their original data before any statistical analysis is carried out and hiding the original data from the statistician. In fact, in local differential privacy, each original data point is only ever seen by the individual it belongs to.
Research in the area focuses on constructing mechanisms to privatise the data that strike the optimal balance between protecting the privacy of the individuals in the study and allowing the best statistical performance. In many cases it is possible to find minimax rates of convergence under this constraint and thus to quantify the statistical cost of privacy. In this talk I will provide an introduction to the field before presenting some new results.

Sponsors:
CREST

Details

Date:
February 19
Time:
2:00 pm - 3:00 pm
Event Categories:
,
Website:
https://statecoml.github.io

Venue

ENSAE ParisTech
5, avenue Henry Le Chatelier
91120 Palaiseau, France

Organizers

Martin Mugnier
François-Pierre Paty
Nicolas Schreuder