text: Minor corrections

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