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conclusion: Reviewed summary

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Manos Katsomallos 2021-11-29 06:31:28 +01:00
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\section{Thesis summary} \section{Thesis summary}
\label{sec:sum-thesis} \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. 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} \paragraph{Survey on continuous data publishing}
@ -15,12 +15,12 @@ This work appeared in the special feature on Geospatial Privacy and Security of
journal of Spatial Information Science~\cite{katsomallos2019privacy}. journal of Spatial Information Science~\cite{katsomallos2019privacy}.
\paragraph{Configurable privacy protection for time series} \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 in the presence of important events. Our contributions are: We presented ($\varepsilon$, $L$)-\emph{{\thething} privacy}, a novel privacy notion that is based on differential privacy allowing for better data utility in the presence of significant events. Our contributions are:
\begin{itemize} \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 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 proposed and formally defined a novel privacy notion, ($\varepsilon$, $L$)-\emph{{\thething} privacy}.
\item We designed and implemented three {\thething} privacy schemes for {\thethings} spanning a finite time series. \item We designed and implemented three {\thething} privacy schemes 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 studied {\thething} privacy under temporal correlation, which is inherent in time series, and observed 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 \item We designed an additional differential privacy mechanism, based on the exponential mechanism, for providing
% additional % additional
protection to the temporal position of the {\thethings} protection to the temporal position of the {\thethings}