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}