2016 - T1 - WS5 - Secrecy and privacy theme

Collection 2016 - T1 - WS5 - Secrecy and privacy theme

Organizer(s) Narayan, Prakash ; Roth, Aaron ; Sarwate, Anand ; Vaikuntanathan, Vinod ; Vadhan, Salil

Date(s) 21/03/2016 - 01/04/2016

linked URL https://web.archive.org/web/20221228152149/http://iss.bu.edu/bobak/csnexus//secrecy.html

00:00:00 / 00:00:00

40 41

The Price of Privacy in High-Dimensional Statistics

By Jonathan Ullman

I will present some new, nearly-optimal lower bounds on the amount of data required to release differentially private statistics on high-dimensional datasets, both in information-theoretic and computational settings. These results show that there is a significant “price of differential privacy” in high-dimensional datasets. We prove these lower bounds using two closely-related cryptographic primitives fingerprinting codes (in information theoretic setting) and traitor-tracing schemes (in the computational setting) that we show are closely connected to differentially private data analysis. I will also discuss how these lower bounds are related to realistic attacks on released datasets.

Information about the video

Date of recording 01/04/2016
Date of publication 14/04/2016
Institution IHP
Licence CC BY-NC-ND
Format MP4

Domain(s)

Computer Science

MSC codes

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