From 6ef8bad61a512eca7160e770cba36fa61f821640 Mon Sep 17 00:00:00 2001
From: Manos Katsomallos <manos@delkappa.com>
Date: Mon, 25 Oct 2021 07:59:02 +0200
Subject: [PATCH] conclusion: Summary

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 text/conclusion/summary.tex | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

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 \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.
+
+
+\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 identified the privacy protection algorithms and techniques that each work is using.
+  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.
+\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 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}.
+  \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.
+\end{itemize}