Differential Privacy in the Shuffle Model
Abstract
Research in differential privacy studies algorithms that offer a rigorous notion of plausible deniability to data contributors. There are a number of models in which these algorithms are executed. This talk will focus on the shuffle model, designed to address shortcomings of two well-studied models. We will explore two protocols for differentially private counting and take a glimpse at lower bound techniques.
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https://umd.zoom.us/j/97585901703
Meeting ID: 975 8590 1703
This talk is organized by David Miller