Resolved missing references

text/thething/problem.tex

@@ -98,7 +98,7 @@ The identification of {\thething} events can be performed manually or automatica
 %We defer the study of the {\thethings} discovery to a following work.
 In this work, we consider the {\thething} timestamps non-sensitive and provided by the user as input along with the privacy budget $\varepsilon$.
 % \kat{check that this is mentioned in the intro}
-For example, events $p_1$, $p_3$, $p_5$, $p_8$ in Figure~\ref{fig:scenario} are {\thething} events.
+For example, events $p_1$, $p_3$, $p_5$, $p_8$ in Figure~\ref{fig:lmdk-scenario} are {\thething} events.
 % relevant to certain user-defined privacy criteria, or to its adjacent data item(s) as well as to the entire data set or parts thereof.
 
 % A significant event or item signals its consequence to us, toward us.
@@ -122,7 +122,7 @@ For example, events $p_1$, $p_3$, $p_5$, $p_8$ in Figure~\ref{fig:scenario} are
 %\end{definition}
 
 Two time series of equal lengths are \emph{{\thething} neighboring} when they differ by a single {\thething} event.
-For example, the time series ($p_1$, \dots, $p_8$) with {\thethings} set the $\{p_1, p_3,p_5\}$ is {\thething} neighboring to the time series of Figure~\ref{fig:scenario}.
+For example, the time series ($p_1$, \dots, $p_8$) with {\thethings} set the $\{p_1, p_3,p_5\}$ is {\thething} neighboring to the time series of Figure~\ref{fig:lmdk-scenario}.
 %This means that we can obtain the first time series by adding/removing one event to/from the second time series.
 %to/from any one of two {\thething} neighboring series of events we can obtain the other series.
 % Therefore, Corollary~\ref{cor:thething-nb} follows.
@@ -147,7 +147,7 @@ We proceed to propose \emph{{\thething} privacy}, a configurable variation of di
 \end{definition}
 
 % \kat{to rephrase for an easier transition -- mention here user and event level that satisfy {\thething} privacy and add discussion that we can do better and propose the new mechanism}
-As discussed in Section~\ref{sec:intro}, user-level privacy can achieve {\thething} privacy, but it over-perturbs the final data by not distinguishing into {\thething} and regular events.
+As discussed in Section~\ref{subsec:prv-levels}, user-level privacy can achieve {\thething} privacy, but it over-perturbs the final data by not distinguishing into {\thething} and regular events.
 Theorem~\ref{theor:thething-prv} proposes how to achieve the desired privacy for the {\thethings} (i.e.,~a total budget lower than $\varepsilon$), and in the same time provide better quality overall. 
  
 %   the existing protection levels of differential privacy do not provide adequate control in time series publishing.