diff --git a/text/evaluation/details.tex b/text/evaluation/details.tex
index ce04e30..2eca13b 100644
--- a/text/evaluation/details.tex
+++ b/text/evaluation/details.tex
@@ -38,7 +38,6 @@ We sampled the first $1000$ data items of the taxi with identifier `$2$'.
 \subsubsection{Synthetic}
 We generated synthetic time series of length equal to $100$ timestamps, for which we varied the number and distribution of {\thethings}.
 We take into account only the temporal order of the points and the position of regular and {\thething} events within the series. 
-% Note, that 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.
 
 
 \subsection{Configurations}
@@ -46,7 +45,7 @@ We take into account only the temporal order of the points and the position of r
 
 \subsubsection{{\Thethings}' percentage}
 
-For Copenhagen, we achieve 
+For the Copenhagen data set, we achieve 
 $0\%$ {\thethings} by considering an empty list of contact devices,
 $20\%$ by extending the list with $[3$, $6$, $11$, $12$, $25$, $29$, $36$, $39$, $41$, $46$, $47$, $50$, $52$, $56$, $57$, $61$, $63$, $78$, $80]$, 
 $40\%$ with $[81$, $88$, $90$, $97$, $101$, $128$, $130$, $131$, $137$, $145$, $146$, $148$, $151$, $158$, $166$, $175$, $176]$, 
@@ -88,9 +87,10 @@ In our experiments, for simplicity, we set $n = 2$ and we investigate the effect
 
 \subsubsection{Privacy parameters}
 
-To perturb the contact tracing data of Copenhagen, we utilize the \emph{random response} technique to report with probability $p = \frac{e^\varepsilon}{e^\varepsilon + 1}$ weather the current contact is a {\thething} or not.
-We randomize them the energy consumption in HUE with the Laplace mechanism.
+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.
diff --git a/text/preliminaries/privacy.tex b/text/preliminaries/privacy.tex
index 033231e..9259948 100644
--- a/text/preliminaries/privacy.tex
+++ b/text/preliminaries/privacy.tex
@@ -291,8 +291,8 @@ queries can be problematic, since a single, outlier value could change the outpu
 
 
 \paragraph{Popular privacy mechanisms}
-
 \label{subsec:prv-mech}
+
 A typical example of a differential privacy mechanism is the \emph{Laplace mechanism}~\cite{dwork2014algorithmic}.
 It draws randomly a value from the probability distribution of $\textrm{Laplace}(\mu, b)$, where $\mu$ stands for the location parameter and $b > 0$ is the scale parameter (Figure~\ref{fig:laplace}).
 In our case, $\mu$ is equal to the original output value of a query function, and $b$ is the sensitivity of the query function divided by $\varepsilon$.