University of Cambridge > Talks.cam > Machine Learning Reading Group @ CUED > An Overview of Differential Privacy, Membership Inference Attacks, and Federated Learning

  Browse A-Z

Log in

Googledetails Microsoftdetails

🔐 Log In

Information on

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

ℹī¸ Information

An Overview of Differential Privacy, Membership Inference Attacks, and Federated Learning

Download to your calendar using vCal

If you have a question about this talk, please contact James Allingham .

Zoom link available upon request (it is sent out on our mailing list, eng-mlg-rcc [at] lists.cam.ac.uk). Sign up to our mailing list for easier reminders.

This tutorial will cover the basics of differential privacy (DP) including the Gaussian mechanism, training networks with DP-SGD, and a look at various state-of-the-art approaches. We then describe the ideas behind membership inference attacks and show how they can be used to audit differentially private systems. Finally, we give an overview of federated learning and explain how it can be made to be differentially private. If there is time remaining, we will present a case study on differentially private speech.

Required Reading: None.

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

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Waterfront Conference Cenre, Wyboston Lakes, Bedfordshire stem cell Eddington Postdoc Centre, Eddington Avenue, Cambridge CB3 1AS

Other talks

Teams Announced Reproducibility In Astrophysical Simulations: The AGORA Initiative and a 14-Year Human Experiment (In Memory of Joel Primack) Morning Break Talk TBC title abstract tbc Caribbean Trip and Cacti
 

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