text: Minor corrections

text/problem/main.tex

@@ -15,7 +15,7 @@ Example~\ref{ex:scenario} shows the result of user--LBS interaction while retrie
 
   Consider a finite sequence of spatiotemporal data generated by Bob during an interval of $8$ timestamps, as shown in Figure~\ref{fig:scenario}.
   Events in a shade correspond to privacy-sensitive 
-  \kat{You should not say that only significant events are privacy sensitive, because then why put noise to the normal timestamps? Maybe say directly significant for the shaded events?} events that Bob has defined beforehand. For instance, $p_1$ and $p_8$ are significant because he was at his home, which is around {\'E}lys{\'e}e, at $p_3$ he was at his workplace around the Louvre, and at $p_5$ he was at his hangout around Canal Saint-Martin.
+  \kat{You should not say that only significant events are privacy-sensitive, because then why put noise to the normal timestamps? Maybe say directly significant for the shaded events?} events that Bob has defined beforehand. For instance, $p_1$ and $p_8$ are significant because he was at his home, which is around {\'E}lys{\'e}e, at $p_3$ he was at his workplace around the Louvre, and at $p_5$ he was at his hangout around Canal Saint-Martin.
 
   \begin{figure}[htp]
     \centering

text/problem/theotherthing/main.tex

@@ -4,7 +4,7 @@
 In Section~\ref{sec:thething}, we introduced the notion of {\thething} events in privacy-preserving time series publishing.
 The differentiation among regular and {\thething} events stipulates a privacy budget allocation that deviates from the application of existing differential privacy protection levels.
 Based on this novel event categorization, we designed three models (Section~\ref{subsec:lmdk-mechs}) that achieve {\thething} privacy.
-For this, we assumed that the timestamps in the {\thething} set $L$ are not privacy sensitive, and therefore we used them in our models as they were.
+For this, we assumed that the timestamps in the {\thething} set $L$ are not privacy-sensitive, and therefore we used them in our models as they were.
 
 This may pose a direct or indirect privacy threat to the data generators (users).
 For the former, we consider the case where we desire to publish $L$ as complimentary information to the release of the event values.

text/problem/theotherthing/solution.tex

@@ -198,8 +198,8 @@ In the next step of the process, we randomly select a set by utilizing the expon
 For this procedure, we allocate a small fraction of the available privacy budget, i.e.,~$1$\% or even less (see Section~\ref{subsec:sel-eps} for more details).
 
 
-\paragraph{Score function}
-Prior to selecting a set, the exponential mechanism evaluates each set using a score function.
+\paragraph{Utility score function}
+Prior to selecting a set, the exponential mechanism evaluates each set using a utility score function.
 
 One way evaluate each set is by taking into account the temporal position the events in the sequence.
 % Nearby events

text/problem/thething/solution.tex

@@ -60,7 +60,7 @@ to the next timestamps.
 
 
 \subsubsection{{\Thething} privacy under temporal correlation}
-\label{subsec:lmdk-cor}
+\label{subsec:lmdk-tpl}
 
 From the discussion so far, it is evident that for the budget distribution it is not the positions but rather the number of the {\thethings} that matters.
 However, this is not the case under the presence of temporal correlation, which is inherent in continuously generated data.