diff --git a/text/evaluation/details.tex b/text/evaluation/details.tex
index f8096b7..4c5a4c1 100644
--- a/text/evaluation/details.tex
+++ b/text/evaluation/details.tex
@@ -65,6 +65,10 @@ In order to get {\thethings} with the above distribution features, we generate p
 For example, for a left-skewed {\thethings} distribution we would utilize a truncated distribution resulting from the restriction of the domain of a distribution to the beginning and end of the time series with its location shifted to the center of the right half of the series.
 For consistency, we calculate the scale parameter depending on the length of the series by setting it equal to the series' length over a constant.
 
+Notice that in our experiments, in the cases when we have $0\%$ and $100\%$ of the events being {\thethings}, we get the same behavior as in event- and user-level privacy respectively.
+This happens due the fact that at each timestamp we take into account only the data items at the current timestamp and ignore the rest of the time series (event-level) when there are no {\thethings}.
+Whereas, when each timestamp corresponds to a {\thething} we consider and protect all the events throughout the entire series (user-level).
+
 
 \subsubsection{Privacy parameters}