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 \section{Contribution}
 \label{sec:contr}
+Our objective is to study the problems around the subject of two contrasting desiderata: quality and privacy in user-generated Big Data.
+We consider the scenario where data are processed/published in a continuous manner and envision a configurable privacy technique that we can tune depending on the use case requirements.
 
-\mk{WIP}
+The main challenge in this setting is maintaining a balance between privacy and utility.
+Furthermore, we consider the presence of temporal correlation, which is inherent in continuous data publishing schemes and can lead to additional privacy loss. 
+
+
+\paragraph{Privacy, space and time}
+The first contribution of this thesis is the survey of the existing literature regarding methods on privacy-preserving continuous data publishing.
+We study works that were published over the past two decades and provide a guide that will navigate its users through the available methodology and help them select the algorithm(s) that fit(s) best their needs.
+
+We categorize the works that we review depending on if they deal with \emph{microdata} or \emph{statistical data}.
+Then, we group them based on the duration of the processing/publishing that they aim for.
+Furthermore, we document in detail the privacy protection characteristics of each reviewed method.
+
+
+\paragraph{{\Thething} privacy}
+Our second contribution is the proposal and formal definition of a novel privacy notion, \emph{{\thething} privacy}.
+Contrary to the existing privacy protection levels, our notion differentiates events between regular and events that a user might consider more privacy-sensitive, i.e.~\emph{{\thethings}}.
+The introduction of {\thethings}, allows for a configurable privacy protection.
+
+First, we design and implement three {\thething} privacy schemes, accounting for {\thethings} spanning a finite time series.
+Thereafter, we investigate {\thething} privacy under temporal correlation, which is inherent in time series publishing, and discuss how {\thethings} can affect the propagation of temporal privacy loss.
+
+
+\paragraph{Dummy {\thething} selection}
+The third contribution of this thesis is the design of a module that extends our {\thething} privacy schemes and provides additional protection to {\thethings}.
+In other words, we answer the question \emph{`How can we protect the fact that we care more about certain events?'}.
+
+We design an additional differential privacy mechanism, based on the exponential mechanism, that we can easily plug-in the proposed existing {\thething} privacy schemes.
+We provide an optimal solution to this problem, which we improve by adopting a heuristic approach, and then implement a more efficient module that relies in partitioning.
+
+
+\bigskip
+We extensively evaluate the methods that we propose by conducting experiments on real and synthetic data sets.
+We compare {\thething} privacy with event- and user-level privacy protection, and investigates the behavior of the overall privacy loss under temporal correlation for different distributions of {\thethings}.
+Furthermore, we estimate the impact of the privacy-preserving dummy {\thething} selection module on the utility of our privacy scheme.