diff --git a/text/problem/thething/problem.tex b/text/problem/thething/problem.tex
index db43148..f89f5f8 100644
--- a/text/problem/thething/problem.tex
+++ b/text/problem/thething/problem.tex
@@ -6,10 +6,13 @@ In this section, we introduce a new privacy definition.
 \subsubsection{Setting}
 \label{subsec:lmdk-set}
 Our problem setting consists of three entities: (i)~data generators (users), (ii)~data publishers (trusted non-adversarial entities), and (iii)~data consumers (possibly adversarial entities). 
-Users generate sensitive data, which are processed in a secure and private way by a trusted curator and are later published in order to be consumed by potentially adversarial data analysts. 
-%The data unit produced by the users is an \emph{event}, i.e., a piece of timestamped user-related information.\kat{should we say geo-stamped?}. 
+Users generate a finite series of sensitive data over time, which are processed in batch mode in a secure and private way locally (or by a trusted curator) and are later published in order to be consumed by potentially adversarial data analysts. 
 Data are produced as a series of events, which we call time series. 
-An \emph{event} is defined as a triple of an identifying attribute of an individual and the possibly sensitive data at a timestamp.
+
+% Users generate sensitive data, which are processed in a secure and private way by a trusted curator and are later published in order to be consumed by potentially adversarial data analysts. 
+%The data unit produced by the users is an \emph{event}, i.e., a piece of timestamped user-related information.\kat{should we say geo-stamped?}. 
+% Data are produced as a series of events, which we call time series. 
+% An \emph{event} is defined as a triple of an identifying attribute of an individual and the possibly sensitive data at a timestamp.
 %This workflow is repeated in a continuous manner, producing series of events, which we call time series. 
 %, producing, processing, publishing, and consuming events in a private manner. 
 \begin{enumerate}[(i)]
@@ -82,7 +85,7 @@ Theorem~\ref{theor:thething-prv} states how to achieve the desired privacy goal
   \label{theor:thething-prv}
   Let $\mathcal{M}$ be a mechanism with input a time series $S_T$, where $T$ is the set of the  involved timestamps, and $L \subseteq T$ be the set of {\thething} timestamps.
   $\mathcal{M}$ is decomposed to $\varepsilon$-differential private sub-mechanisms $\mathcal{M}_t$, for every $t \in T$, which apply independent randomness to the event at $t$.
-  Then, given a privacy budget $\varepsilon$, $\mathcal{M}$ satisfies {\thething} privacy if for any $t$ it holds that
+  Then, given a privacy budget $\varepsilon$, $\mathcal{M}$ satisfies $(\varepsilon, L)$-\emph{{\thething} privacy} if for any $t$ it holds that
   $$ \sum_{i\in L \cup \{t\}} \varepsilon_i \leq \varepsilon$$
 \end{theorem}