manos/the-last-thing
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

conclusion: Reviewed summary

Browse Source
This commit is contained in:
Manos Katsomallos 2021-11-29 06:31:28 +01:00
parent bbff517afb
commit 04b7f4826f
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
text/conclusion/summary.tex
View File

@ -1,6 +1,6 @@
\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.
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}
@ -15,12 +15,12 @@ This work appeared in the special feature on Geospatial Privacy and Security of
journal of Spatial Information Science~\cite{katsomallos2019privacy}.
\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}
\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 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
% additional
protection to the temporal position of the {\thethings}