introduces some relevant terminology and information around the problem of
quality and privacy in user-generated Big Data with a special focus on continuous data publishing.
First, in Section~\ref{sec:data}, we categorize user-generated data sets and review data processing in the context of continuous data publishing.
Second, in Section~\ref{sec:privacy}, we define information disclosure in data privacy. We list the categories of privacy attacks, the possible privacy protection levels, the fundamental privacy operations that are applied to achieve data privacy, and finally we provide a brief overview of the basic notions for data privacy protection.
Third, in Section~\ref{sec:correlation}, we focus on the impact of correlation on data privacy.
More particularly, we discuss the different types of correlation, we document ways to extract data correlation from continuous data, and we investigate the privacy risks that data correlation entails with special focus on the privacy loss under temporal correlation.
\paragraph{Chapter~\ref{ch:rel}}
reviews works that deal with privacy under continuous data publishing covering diverse use cases.
We present the relevant literature based on two levels of categorization.
First, we group works with respect to whether they deal with microdata or statistical data as input.
Then, we further group them into two subcategories depending on if they are designed for the finite or infinite observation setting.
puts forward a novel configurable privacy scheme, \emph{{\thething} privacy} (Section~\ref{sec:thething}), which takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly.
We propose three privacy schemes that guarantee {\thething} privacy.
To further enhance our privacy methodology, and protect the {\thething} position in the time series, we propose techniques to perturb the initial {\thething} set (Section~\ref{sec:theotherthing}).
\paragraph{Chapter~\ref{ch:eval}}
presents the experiments that we performed in order to evaluate {\thething} privacy (Chapter~\ref{ch:lmdk-prv}) on real and synthetic data sets.
Section~\ref{sec:eval-dtl} contains all the details regarding the data sets the we used for our experiments along with the system configurations.
Section~\ref{sec:eval-lmdk} evaluates the data utility of the {\thething} privacy schemes that we designed in Section~\ref{sec:thething} and investigates the behavior of the privacy loss under temporal correlation for different distributions of {\thethings}.
Section~\ref{sec:eval-lmdk-sel} justifies our decisions while designing the privacy-preserving {\thething} selection module in Section~\ref{sec:theotherthing} and the data utility impact of the latter.
Finally, Section~\ref{sec:eval-sum} concludes this chapter by summarizing the main results derived from the experiments.
\paragraph{Chapter~\ref{ch:con}}
concludes the thesis and outlines possible future directions.
