conclusion: Reviewed summary

text/conclusion/summary.tex

@@ -1,23 +1,22 @@
 \section{Thesis summary}
 \label{sec:sum-thesis}
-This thesis provides solutions for quality and privacy in user-generated Big Data focusing on the problems regarding privacy-preserving continuous data publishing  that we summarize below.
+This thesis revolves around the topic of quality and privacy in user-generated Big Data focusing on the problems regarding privacy-preserving continuous data publishing  that we summarize below.
 
 
 \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.
 \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 categorized each data category based on its length in \emph{finite} and \emph{infinite}.
+  \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.
+  \item We identified the privacy protection algorithms and techniques that each work is using, focusing on the privacy method, operation, attack, and protection level.
-  Most particularly, we focused 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.
 \end{itemize}
 
 
 \paragraph{Configurable privacy protection for time series}
-We presented \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.
 \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.
+  \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 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 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}.