\section{Thesis summary}
\label{sec:sum-thesis}
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 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.
\end{itemize}


\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.
\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 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}
