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Manos Katsomallos 2021-10-12 13:17:29 +02:00
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text/evaluation/summary.tex
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\section{Summary} \section{Summary}
\label{sec:eval-sum} \label{sec:eval-sum}
In this chapter we presented the experimental evaluation of the {\thething} privacy mechanisms and privacy-preserving {\thething} selection mechanism, that we developed in chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets. In this chapter we presented the experimental evaluation of the {\thething} privacy mechanisms and privacy-preserving {\thething} selection mechanism, that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets.
The Adaptive mechanism is the most reliable and best performing mechanism, in terms of overall data utility, with minimal tuning across most cases. The Adaptive mechanism is the most reliable and best performing mechanism, in terms of overall data utility, with minimal tuning across most cases.
Skip performs optimally in data sets with a lower value range where approximation fits best. Skip performs optimally in data sets with a lower value range where approximation fits best.
The {\thething} selection component introduces a reasonable data utility decline to all of our mechanisms however, the Adaptive handles it well and bounds the data utility to higher levels compared to user-level protection. The {\thething} selection component introduces a reasonable data utility decline to all of our mechanisms however, the Adaptive handles it well and bounds the data utility to higher levels compared to user-level protection.

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text/related/main.tex
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@ -17,7 +17,7 @@ For example, Zhou et al.~\cite{zhou2008brief} have a focus on social networks, a
In this chapter, we document works that deal with privacy under continuous data publishing covering diverse use cases. In this chapter, we document works that deal with privacy under continuous data publishing covering diverse use cases.
We present the works in the literature based on two levels of categorisation. We present the works in the literature based on two levels of categorisation.
First, we group works w.r.t. whether they receive microdata or statistical data (see Section~\ref{subsec:data-categories} for the definitions) as input. First, we group works with respect to whether they receive microdata or statistical data (see Section~\ref{subsec:data-categories} for the definitions) as input.
Then, we further group them into two subcategories, whether they are designed for the finite or infinite (see Section.~\ref{subsec:data-publishing}) observation setting. \kat{continue.. say also in which category you place your work} Then, we further group them into two subcategories, whether they are designed for the finite or infinite (see Section.~\ref{subsec:data-publishing}) observation setting. \kat{continue.. say also in which category you place your work}
%Such a documentation becomes very useful nowadays, due to the abundance of continuously user-generated data sets that could be analyzed and/or published in a privacy-preserving way, and the quick progress made in this research field. %Such a documentation becomes very useful nowadays, due to the abundance of continuously user-generated data sets that could be analyzed and/or published in a privacy-preserving way, and the quick progress made in this research field.