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.
We studied the existing literature regarding methods on privacy-preserving continuous data publishing, spanning the past two decades, while elaborating on data correlation.
\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.
We presented ($\varepsilon$, $L$)-\emph{{\thething} privacy}, a novel privacy notion that is based on differential privacy allowing for better data utility.
\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.
