Next Meetup Scheduled: Differential Privacy for Free? Harnessing the Noise In Approximate Homomorphic Encryption
on June 8th, 4pm Paris - FR time
Greetings cryptographers!
Next week’s Meetup is scheduled for Thursday, June 8th at 4pm Paris - FR time.
The meetup features Tabitha Ogilvie presenting Differential Privacy for Free? Harnessing the Noise In Approximate Homomorphic Encryption.
Here is the link to register:
Abstract
In this meetup, we'll make a connection between two important ideas in the Privacy Enhancing Technologies ecosystem -- Homomorphic Encryption and Differential Privacy. While Homomorphic Encryption ensures that sensitive data is not exposed during computation, Differential Privacy guarantees that each data subject can maintain their privacy when we share the result of that computation.
During this talk, we'll look at noise growth in Homomorphic Encryption (HE), and investigate the possibility that this inherent noise can give Differential Privacy (DP) "for free". We will recap what we mean by HE, noise, and DP, before examining new results on the DP guarantees of the Approximate HE setting. We'll finish by applying our results to a case study: Ridge Regression Training via Gradient Descent.
About the speaker
Tabitha Ogilvie is a PhD student in the Information Security Group at Royal Holloway, University of London, and has just completed a year long internship at Intel, as part of the Security and Privacy Research Group within Intel Labs. Her area of research is Privacy Enhancing Technologies and Privacy Preserving Machine Learning, with a focus on Homomorphic Encryption.
Relevant paper
📄 https://eprint.iacr.org/2023/701
Resources from last meetup
You can access resources from the last meetup Efficient TFHE Bootstrapping in the Multiparty Setting on the FHE.org website here.
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The FHE.org team