diff --git a/text/conclusion/main.tex b/text/conclusion/main.tex
index 4352770..59934ee 100644
--- a/text/conclusion/main.tex
+++ b/text/conclusion/main.tex
@@ -2,15 +2,14 @@
 \label{ch:con}
 Continuous publishing of data, also known as time series, has found over the past decades several application domains, including healthcare, smart building, and traffic monitoring.
 In many cases, time series contain personal details (and are usually geotagged), and thus their processing entails privacy concerns.
+%The processing/publishing of user-generated data in the form of time series, poses privacy risks to the individuals involved.\kat{the deterioration of the quality that we deal with here, is because of the privacy protection of the data, not because of the processing/publishing. I rephrased, together with the next sentence.}
+Several methods have been proposed in order to protect the privacy of individuals while processing their data, but cannot avoid to deteriorate arbitrarily the quality therein. Out of these methods, we distinguish differential privacy, which quantifies the balance between user protection and data utility by a factor $\epsilon$.
 
-The processing/publishing of user-generated data in the form of time series, may not only pose privacy risks to the individuals involved but also deteriorate arbitrarily the quality therein.
-To this end, differential privacy is the most prominent privacy method that can efficiently balance between user protection and data utility.
-
-In this thesis, we have concentrated on continuous user-generated data publishing.
+In this thesis, we have concentrated on continuous user-generated data publishing. \kat{say exactly what case you covered, this is way to general. and connect it to the differential privacy that you previously mentioned, otherwise it seems irrelevant.}
 We have studied the relevant literature with special emphasis on data correlation.
 Furthermore, we explored ways to provide configurable protection in such settings and developed relevant solutions.
 
-Next, we summarize this thesis in the individual chapters by describing our contribution to the problems surrounding quality and privacy in user-generated Big Data.
+Next, we summarize this thesis in the individual chapters by describing our contribution to the problems surrounding quality and privacy in user-generated Big Data\kat{??? be specific, this is the conclusions chapter. 'The problems surrounding quality and privacy in user-generated Big Data ' means nothing.. }.
 Subsequently, we discuss interesting perspectives and open questions for future investigation.
 
 
diff --git a/text/conclusion/summary.tex b/text/conclusion/summary.tex
index e45ec37..c08a6bc 100644
--- a/text/conclusion/summary.tex
+++ b/text/conclusion/summary.tex
@@ -4,21 +4,23 @@ This thesis revolves around the topic of quality and privacy in user-generated B
 
 
 \paragraph{Survey on continuous data publishing}
-We studied the existing literature regarding methods on privacy-preserving continuous data publishing, spanning the past two decades, while elaborating on data correlation.
+We reviewed the existing literature regarding methods on privacy-preserving continuous data publishing, spanning the past two decades, while elaborating on data correlation. Our contributions are:
 \begin{itemize}
   \item We categorized the works that we reviewed based on their input data in either \emph{microdata} or \emph{statistical data} and further separated each data category based on its observation span in \emph{finite} and \emph{infinite}.
-  \item We identified the privacy protection algorithms and techniques that each work is using, focusing on the privacy method, operation, attack, and protection level.
-  \item We organized the reviewed literature in tabular form to allow for a more efficient indexation of the information therein.
+  \item We identified the privacy protection algorithms and techniques that each work is using, focusing on feature like the privacy method, operation, attack, and protection level.
+  \item We organized the reviewed literature in a tabular form to allow for a more efficient indexation of the related works, using a number of relevant features. 
 \end{itemize}
 
+\kat{mention here again that the work appears in the article... in the journal...}
 
 \paragraph{Configurable privacy protection for time series}
-We presented ($\varepsilon$, $L$)-\emph{{\thething} privacy}, a novel privacy notion that is based on differential privacy allowing for better data utility.
+We presented ($\varepsilon$, $L$)-\emph{{\thething} privacy}, a novel privacy notion that is based on differential privacy allowing for better data utility in the presence of important events. Our contributions are:
 \begin{itemize}
   \item We introduced the notion of \emph{{\thething} events} in privacy-preserving data publishing and differentiated events between regular and events that a user might consider more privacy-sensitive (\emph{\thethings}).
   % \item We proposed and formally defined a novel privacy notion, ($\varepsilon$, $L$)-\emph{{\thething} privacy}.
-  \item We designed and implemented three {\thething} privacy schemes, accounting for {\thethings} spanning a finite time series.
+  \item We designed and implemented three {\thething} privacy schemes for {\thethings} spanning a finite time series.
   \item We investigated {\thething} privacy under temporal correlation, which is inherent in time series, and studied the effect of {\thethings} on the temporal privacy loss propagation.
-  \item We designed an additional differential privacy mechanism, based on the exponential mechanism, for providing additional protection to the temporal position of the {\thethings}.
-  \item We experimentally evaluated our proposal on real and synthetic data sets, and compared {\thething} privacy schemes with event- and user-level privacy protection, for different {\thething} percentages.
+  \item We designed an additional differential privacy mechanism, based on the exponential mechanism, for providing additional protection to the temporal position of the {\thethings}. \kat{what is the name of the mechanism? how do you quantify 'additional' ?}
+  \item We experimentally evaluated our proposal on real and synthetic data sets, and compared {\thething} privacy schemes with event- and user-level privacy protection, for different {\thething} percentages. \kat{what are the conclusions that show the quality/benefits of the proposed solution?}
 \end{itemize}
+\kat{mention here again that the work appears in the article... submitted at...}
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