diff --git a/text/related/statistical.tex b/text/related/statistical.tex
index a8e472d..480b57d 100644
--- a/text/related/statistical.tex
+++ b/text/related/statistical.tex
@@ -117,7 +117,7 @@ Nonetheless, as with all other similar techniques, the usage of prefix trees lim
 % - \emph{Pufferfish}
 % - perturbation (Laplace)
 % - general (Bayesian networks/Markov chains)
-\hypertarget{song2017pufferfish}{Song et al.}~\cite{song2017pufferfish} propose the \emph{Wasserstein mechanism}, a technique that applies to any general instantiation of Pufferfish (see Section~\ref{subsec:privacy-statistical}).
+\hypertarget{song2017pufferfish}{Song et al.}~\cite{song2017pufferfish} propose the \emph{Wasserstein mechanism}, a technique that applies to any general instantiation of Pufferfish (see Section~\ref{subsec:prv-statistical}).
 It adds noise proportional to the sensitivity of a query $F$, which depends on the worst case distance between the distributions $P(F(X)|s_i,d)$ and $P(F(X)|s_j,d)$ for a variable $X$, a pair of secrets $(s_i,s_j)$, and an evolution scenario $d$. 
 The Wasserstein metric function calculates the worst case distance between those two distributions.
 The noise is drawn from a Laplace distribution with parameter equal to the quotient resulting from the division of the maximum Wasserstein distance of the distributions of all the pairs of secrets by the available privacy budget $\varepsilon$.
diff --git a/text/thething/problem.tex b/text/thething/problem.tex
index 43054c3..cbfab0b 100644
--- a/text/thething/problem.tex
+++ b/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.