26 lines
2.2 KiB
TeX
26 lines
2.2 KiB
TeX
\section{Thesis summary}
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\label{sec:sum-thesis}
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This thesis provides solutions for quality and privacy in user-generated Big Data focusing on the problems regarding privacy-preserving continuous data publishing that we summarize below.
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\paragraph{Survey on continuous data publishing}
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We studied the existing literature regarding methods on privacy-preserving continuous data publishing, spanning the past two decades, while elaborating on data correlation.
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\begin{itemize}
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\item We categorized the works that we reviewed based on their input data in either \emph{microdata} or \emph{statistical data} and further categorized each data category based on its length in \emph{finite} and \emph{infinite}.
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\item We identified the privacy protection algorithms and techniques that each work is using.
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Most particularly, we focused on the privacy method, operation, attack, and protection level.
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\item We organized the reviewed literature in tabular form to allow for a more efficient indexation of the information therein.
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\end{itemize}
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\paragraph{Configurable privacy protection for time series}
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We presented \emph{\thething} privacy, a novel privacy notion that is based on differential privacy allowing for better data utility.
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\begin{itemize}
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\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.
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\item We proposed and formally defined a novel privacy notion, ($\varepsilon$, $L$)-\emph{{\thething} privacy}.
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\item We designed and implemented three {\thething} privacy schemes, accounting for {\thethings} spanning a finite time series.
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\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.
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\item We designed an additional differential privacy mechanism, based on the exponential mechanism, for providing additional protection to the temporal position of the {\thethings}.
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\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.
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\end{itemize}
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