From b765a8b0695b51cefc8a03b27747365f3b57528a Mon Sep 17 00:00:00 2001 From: Manos Katsomallos Date: Sat, 15 Jan 2022 03:36:45 +0100 Subject: [PATCH] privacy: Minor corrections --- text/preliminaries/privacy.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/text/preliminaries/privacy.tex b/text/preliminaries/privacy.tex index b944e8e..293fa37 100644 --- a/text/preliminaries/privacy.tex +++ b/text/preliminaries/privacy.tex @@ -340,9 +340,9 @@ The technique adds random noise drawn from a multivariate Laplace distribution t \label{fig:mech-planar-lap} \end{figure} -For query functions that do not return a real number, e.g.,~`What is the most visited country this year?' or in cases where perturbing the value of the output will completely destroy its utility, e.g.,~`How many patients in the ICU?' most works in the literature use the \emph{Exponential mechanism}~\cite{mcsherry2007mechanism}. +For query functions that do not return a real number, e.g.,~`What is the most visited country this year?', or in cases where perturbing the value of the output will completely destroy its utility, e.g.,~`How many patients in the ICU?', most works in the literature use the \emph{Exponential mechanism}~\cite{mcsherry2007mechanism}. Initially, a utility function $u$, with sensitivity $\Delta u$, maps pairs of the input value $x$ and output value $r$ to utility scores. -Thereafter, the mechanism $M$ selects an output value $r$ from a set of possible outputs $R$ with probability proportional to $\exp(\frac{\varepsilon u(x, r)}{2\Delta u})$ (Figure~\ref{fig:mech-exp}). +Thereafter, the mechanism $M$ selects an output value $r$ from a set of possible outputs $R$ with probability proportional to $\exp(\frac{\varepsilon u(x, r)}{2\Delta u})$. % $\Delta u$is the sensitivity of the utility % \kat{what is the utility function?} % \mk{Already explained}