diff --git a/text/problem/theotherthing/solution.tex b/text/problem/theotherthing/solution.tex
index dfa9185..fe37676 100644
--- a/text/problem/theotherthing/solution.tex
+++ b/text/problem/theotherthing/solution.tex
@@ -42,16 +42,13 @@ It finds the option that is the most \emph{similar} to the original (Lines~{\ref
   % Evaluate the original
   \evalOrig $\leftarrow$ \evalSeq{$T, \emptyset, L$}\;
 
-  % Get all possible option combinations
-  \opts $\leftarrow$ \getOpts{$T, L$}\;
-
   % Track the minimum (best) evaluation
   \diffMin $\leftarrow$ $\infty$\;
 
   % Track the optimal sequence (the one with the best evaluation)
-  \optim $\leftarrow$ $[]$\;
+  \opts $\leftarrow$ $[]$\;
 
-  \ForEach{\opt $\in$ \opts}{ \label{algo:lmdk-sel-opt-for-each}
+  \ForEach{\opt $\in$ \getOpts{$T, L$}}{ \label{algo:lmdk-sel-opt-for-each}
     \evalCur $\leftarrow 0$\;
     \ForEach{\opti $\in$ \opt}{
       \evalCur $\leftarrow$ \evalCur $+$ \evalSeq{$T, \opti, L$}/\#\opt\; \label{algo:lmdk-sel-opt-comparison}
@@ -60,10 +57,10 @@ It finds the option that is the most \emph{similar} to the original (Lines~{\ref
     \diffCur $\leftarrow \left|\evalCur - \evalOrig\right|$\;
     \If{\diffCur $<$ \diffMin}{
       \diffMin $\leftarrow$ \diffCur\;
-      \optim $\leftarrow$ \opt\;
+      \opts $\leftarrow$ \opt\;
     }
   } \label{algo:lmdk-sel-opt-end}
-  \Return{\optim}
+  \Return{\opts}
 \end{algorithm}
 
 Algorithm~\ref{algo:lmdk-sel-opt} guarantees to return the optimal set of dummy {\thethings} with regard to the original set $L$.
@@ -82,7 +79,7 @@ At each step it selects a new timestamp, that corresponds to a regular ({non-\th
   \DontPrintSemicolon
 
   \KwData{$T, L$}
-  \KwResult{\optim}
+  \KwResult{\opts}
   \BlankLine
 
   % Evaluate the original
@@ -196,18 +193,25 @@ In the end of the process, we return \opts which contains all the versions of \h
 \subsubsection{Privacy-preserving option selection}
 \label{subsec:lmdk-opt-sel}
 
-\mk{WIP}
+The Algorithms of Section~\ref{subsec:lmdk-set-opts} return a set of possible versions of the original {\thething} set $L$ by adding extra timestamps in it from the series of events at timestamps $T \supseteq L$.
+In the next step of the process, we randomly select a set by utilizing the exponential mechanism (Section~\ref{subsec:prv-mech}).
+Prior to selecting a set, the exponential mechanism evaluates each set using a score function.
 
+One way evaluate each set is by taking into account the temporal position the events in the sequence.
 % Nearby events
 Events that occur at recent timestamps are more likely to reveal sensitive information regarding the users involved~\cite{kellaris2014differentially}.
 Thus, taking into account more recent events with respect to {\thethings} can result in less privacy loss and better privacy protection overall.
 This leads to worse data utility.
-
 % Depending on the {\thething} discovery technique
 The values of events near a {\thething} are usually similar to that of the latter.
 Therefore, privacy-preserving mechanisms are likely to approximate their values based on the nearest {\thething} instead of investing extra privacy budget to perturb their actual values; thus, spending less privacy budget.
 Saving privacy budget for releasing perturbed versions of actual event values can bring about better data utility. 
-
 % Distant events
-However, indicating the existence of randomized/dummy {\thethings} nearby actual {\thethings} can increase the adversarial confidence regarding the location of the latter within a series of events.
-Hence, choosing randomized/dummy {\thethings} far from the actual {\thethings} (and thus less relevant) can limit the final privacy loss.
+However, indicating the existence of dummy {\thethings} nearby actual {\thethings} can increase the adversarial confidence regarding the location of the latter within a series of events.
+Hence, choosing dummy {\thethings} far from the actual {\thethings} (and thus less relevant) can limit the final privacy loss.
+
+Another approach for the score function is to consider the number of events in each set.
+On the one hand, sets with more dummy {\thethings} may render actual {\thethings} more indistinguishable probabilistically.
+That is due to the fact that, it is harder for an adversary to pick a {\thething} when the ratio of {\thethings} to the size of the set gets lower.
+On the other hand, more dummy {\thethings} lead to distributing the privacy budget to more events, and therefore investing less at each timestamp.
+Thus, providing a better level of privacy protection.