University of Cambridge > Talks.cam > Machine Learning @ CUED > Differential Privacy Tutorial

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹ️ Information

Differential Privacy Tutorial

Download to your calendar using vCal

If you have a question about this talk, please contact Alexander Matthews .

Abstract:

Differential privacy is the dominant theory of statistical analysis under constraints of individual privacy preservation. In the first section, which will constitute the majority of the talk, we will introduce the basic concepts of Differential Privacy and its motivation. It will be similar to the first chapters of

1) The Algorithmic Foundations of Differential Privacy. Dwork and Roth. 2014

However, since our talk is introductory in nature no prior reading will be necessary.

To give a flavour of recent research, we will then give a brief overview of two recent papers on the topic specifically:

2) Deep Learning with Differential Privacy. Abadi et al. 2016

3) Differential Privacy For Functions and Functional Data. Hall et al. 2013

This talk is part of the Machine Learning @ CUED series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Fletcher-Moulton Room at Wolfson Court, Clarkson Road Seminar Room 3, Alan Reece Building Institute for Manufacturing (IfM), 17 Charles Babbage Road, Cambridge CB3 0FS Rooms SG1 & SG2, Alison Richard Building, 7 West Road, Cambridge, CB3 9DT

Other talks

When Programmes Disagree About Programmes: Code Poetry and the Problem of Programme–Programme Communication Title tbc Whether and when: Split-population cure models of women's work and parity progression in Derbyshire, 1881-1911 Group Work Trigger-Level Analysis at ATLAS Thelocactus
 

© 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity