Welcome to the resource topic for 2014/982
Title:
Outlier Privacy
Authors: Edward Lui, Rafael Pass
Abstract:We introduce a generalization of differential privacy called \emph{tailored differential privacy}, where an individual’s privacy parameter is tailored'' for the individual based on the individual's data and the data set. In this paper, we focus on a natural instance of tailored differential privacy, which we call \emph{outlier privacy}: an individual's privacy parameter is determined by how much of an \emph{outlier}‘’ the individual is. We provide a new definition of an outlier and use it to introduce our notion of outlier privacy. Roughly speaking, \emph{\eps(\cdot)-outlier privacy} requires that each individual in the data set is guaranteed $\eps(k)$-differential privacy protection'', where $k$ is a number quantifying the outlierness’’ of the individual. We demonstrate how to release accurate histograms that satisfy \eps(\cdot)-outlier privacy for various natural choices of \eps(\cdot). Additionally, we show that \eps(\cdot)-outlier privacy with our weakest choice of \eps(\cdot)—which offers no explicit privacy protection for non-outliers''---already implies a distributional’’ notion of differential privacy w.r.t.~a large and natural class of distributions.
ePrint: https://eprint.iacr.org/2014/982
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