diff --git a/graphics/evaluation/copenhagen-sel-eps.pdf b/graphics/evaluation/copenhagen-sel-eps.pdf
new file mode 100644
index 0000000..4dd0623
Binary files /dev/null and b/graphics/evaluation/copenhagen-sel-eps.pdf differ
diff --git a/graphics/evaluation/copenhagen-sel.pdf b/graphics/evaluation/copenhagen-sel.pdf
index 5db4b5b..08b1d46 100644
Binary files a/graphics/evaluation/copenhagen-sel.pdf and b/graphics/evaluation/copenhagen-sel.pdf differ
diff --git a/graphics/evaluation/hue-sel-eps.pdf b/graphics/evaluation/hue-sel-eps.pdf
new file mode 100644
index 0000000..cf16340
Binary files /dev/null and b/graphics/evaluation/hue-sel-eps.pdf differ
diff --git a/text/evaluation/theotherthing.tex b/text/evaluation/theotherthing.tex
index e40ce6f..b17a808 100644
--- a/text/evaluation/theotherthing.tex
+++ b/text/evaluation/theotherthing.tex
@@ -33,6 +33,35 @@ While both methods share the same mean normalized distance of $0.4$, the Euclide
 Therefore, we choose to utilize the Euclidean distance metric for the implementation of the privacy-preserving {\thething} selection in Section~\ref{subsec:lmdk-sel-sol}.
 
 
+\subsection{Privacy budget tuning}
+\label{subsec:sel-eps}
+
+In Figure~\ref{fig:sel-eps} we test the Uniform model in real data by investing different ratios ($1$\%, $10$\%, $25$\%, and $50$\%) of the available privacy budget $\varepsilon$ in the {\thething} selection mechanism, in order to figure out the optimal ratio value.
+Uniform is our baseline implementation, and hence allows us to derive more accurate conclusions in this case.
+In general, greater ratios will result in more accurate, i.e.,~smaller, {\thething} sets and less accurate values in the released data sets.
+
+\begin{figure}[htp]
+  \centering
+  \subcaptionbox{Copenhagen\label{fig:copenhagen-sel-eps}}{%
+    \includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel-eps}%
+  }%
+  \hspace{\fill}
+  \\ \bigskip
+  \subcaptionbox{HUE\label{fig:hue-sel-eps}}{%
+    \includegraphics[width=.5\linewidth]{evaluation/hue-sel-eps}%
+  }%
+  \subcaptionbox{T-drive\label{fig:t-drive-sel-eps}}{%
+    \includegraphics[width=.5\linewidth]{evaluation/t-drive-sel-eps}%
+  }%
+  \caption{The mean absolute error (a)~as a percentage, (b)~in kWh, and (c)~in meters of the released data for different {\thething} percentages. We apply the Uniform {\thething} privacy model and vary the ratio of the privacy budget $\varepsilon$ that we allocate to the {\thething} selection component.}
+  \label{fig:sel-eps}
+\end{figure}
+
+The application of the randomized response mechanism, in the Copenhagen data set, is tolerant to the fluctuations of the privacy budget.
+For HUE and T-drive, we observe that our implementation performs better for lower ratios, e.g.,~$0.01$, where we end up allocating the majority of the available privacy budget to the data release process instead of the {\thething} selection component.
+The results of this experiment indicate that we can safely allocate the majority of $\varepsilon$ for publishing the data values, and therefore achieve better data utility, while providing more robust privacy protection to the {\thething} timestamp set.
+
+
 \subsection{Budget allocation and {\thething} selection}
 \label{subsec:sel-prv}
 
@@ -44,6 +73,7 @@ Figure~\ref{fig:real-sel} exhibits the performance of Skip, Uniform, and Adaptiv
     \includegraphics[width=.5\linewidth]{evaluation/copenhagen-sel}%
   }%
   \hspace{\fill}
+  \\ \bigskip
   \subcaptionbox{HUE\label{fig:hue-sel}}{%
     \includegraphics[width=.5\linewidth]{evaluation/hue-sel}%
   }%