diff --git a/text/evaluation/details.tex b/text/evaluation/details.tex
index 2eca13b..f8096b7 100644
--- a/text/evaluation/details.tex
+++ b/text/evaluation/details.tex
@@ -65,6 +65,18 @@ In order to get {\thethings} with the above distribution features, we generate p
 For example, for a left-skewed {\thethings} distribution we would utilize a truncated distribution resulting from the restriction of the domain of a distribution to the beginning and end of the time series with its location shifted to the center of the right half of the series.
 For consistency, we calculate the scale parameter depending on the length of the series by setting it equal to the series' length over a constant.
 
+
+\subsubsection{Privacy parameters}
+
+To perturb the contact tracing data of the Copenhagen data set, we utilize the \emph{random response} technique~\cite{wang2017locally} to report with probability $p = \frac{e^\varepsilon}{e^\varepsilon + 1}$ whether the current contact is a {\thething} or not.
+We randomize the energy consumption in HUE with the Laplace mechanism (described in detail in Section~\ref{subsec:prv-mech}).
+We inject noise to the spatial values in T-drive that we sample from the Planar Laplace mechanism~\cite{andres2013geo}.
+
+We set the privacy budget $\varepsilon = 1$, and, for simplicity, we assume that for every query sensitivity it holds that $\Delta f = 1$. 
+For the experiments performed on the synthetic data sets, the original values to be released do not influence the outcome of our conclusions, thus we ignore them.
+% Finally, notice that, depending on the results' variation, most diagrams are in logarithmic scale.
+
+
 \subsubsection{Temporal correlation}
 
 We model the temporal correlation in the synthetic data as a \emph{stochastic matrix} $P$, using a \emph{Markov Chain}~\cite{gagniuc2017markov}.
@@ -84,13 +96,3 @@ The value of $s$ is comparable only for stochastic matrices of the same size and
 the stronger the correlation degree.
 %In general, larger transition matrices tend to be uniform, resulting in weaker correlation.
 In our experiments, for simplicity, we set $n = 2$ and we investigate the effect of \emph{weak} ($s = 1$), \emph{moderate} ($s = 0.1$), and \emph{strong} ($s = 0.01$) temporal correlation degree on the overall privacy loss.
-
-\subsubsection{Privacy parameters}
-
-To perturb the contact tracing data of the Copenhagen data set, we utilize the \emph{random response} technique~\cite{wang2017locally} to report with probability $p = \frac{e^\varepsilon}{e^\varepsilon + 1}$ whether the current contact is a {\thething} or not.
-We randomize the energy consumption in HUE with the Laplace mechanism (described in detail in Section~\ref{subsec:prv-mech}).
-To perturb the spatial values in T-drive, we inject noise that we sample from the Planar Laplace mechanism~\cite{andres2013geo}.
-
-We set the privacy budget $\varepsilon = 1$, and, for simplicity, we assume that for every query sensitivity it holds that $\Delta f = 1$. 
-For the experiments performed on the synthetic data sets, the original values to be released do not influence the outcome of our conclusions, thus we ignore them.
-% Finally, notice that, depending on the results' variation, most diagrams are in logarithmic scale.