Join Ms. Kelly Ramsay, a PhD student from University of Waterloo for their talk on Thursday, March 4th about “Differential Privacy & Data Depth Functions”.


The goal of the talk is to give a gentle introduction to differential privacy and present some of my recent research related to private inference. Differential privacy is a popular standard of privacy recently employed by many large institutions, notably the US census bureau and Google. We touch briefly on the origins of differential privacy before discussing some basic building blocks for constructing private algorithms. Several connections between differential privacy and robust statistics have been identified in the literature. My recent work explores one such connection: privatizing data depth functions. We present some general methods for privatizing depth functions and their associated medians. We also analyse the utility-privacy trade-off with some asymptotic results. Privatization implies the immediate privatization of inference procedures based on data depth.

DATE: Thursday, March 4th, 2021

WHERE: Zoom (see below for more information)

WHEN: 2:30pm – waiting room will open at 2:15pm.

If you wish to attend a Statistics Seminar please contact Po Yang (¬†from the U of M’s Statistics Department for more details and Zoom meeting information.

Feb 24, 2021