Explore a 17-minute IEEE conference talk that delves into the concept of differentially private histograms within the shuffle model, focusing on the use of fake users. Learn from Albert Cheu of Georgetown University and Maxim Zhilyaev from MindStrong Inc. as they present their research on this innovative approach to data privacy and analysis.
Differentially Private Histograms in the Shuffle Model from Fake Users
Overview
Syllabus
Differentially Private Histograms in the Shuffle Model from Fake Users
Taught by
IEEE Symposium on Security and Privacy
Tags
Related Courses
-
Spectral-DP - Differentially Private Deep Learning through Spectral Perturbation and Filtering
-
Learning Differentially Private Mechanisms
-
A Theory to Instruct Differentially-Private Learning via Clipping Bias Reduction
-
Locally Differentially Private Frequent Itemset Mining
-
The Power of Differentially Oblivious Shuffle in Distributed Privacy Mechanisms
-
Locally Differentially Private Frequency Estimation Based on Convolution Framework
