3.1. paragraph comment

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katerinatzo 2021-10-08 15:33:35 +02:00
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}
\caption{Summary table of reviewed privacy-preserving algorithms for continuous microdata publishing.
Location-specific techniques are listed in bold.}
Location-specific techniques are listed in bold.\kat{
do you still need to have in bold the location specific techniques? if yes mention why in the text..}}
\label{tab:micro}

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@ -18,11 +18,11 @@ 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.
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.
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}
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.
\kat{The related work section of your thesis, should make a connection/comparison to your work. This means that you should position the works presented wrt your problem and your solution if the problems are the same. }
\kat{The related work section of your thesis, should make a connection/comparison to your work. This means that you should position the works presented wrt your problem and your solution if the problems are the same. Put a small (or big) paragraph in the end of each of the two sections (microdata and statistical data) and name the similarities/differences }
\input{related/micro}
\input{related/statistical}

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@ -458,3 +458,6 @@ The goal is to minimize the information throughput and always answer users' requ
They model the dependence between requests using a Markov chain, which is publicly known, where each state represents an available service.
Setting privacy to ON, the user obfuscates their original query by randomly sending requests to (and receiving answers from) a subset of all of the available services.
Although this randomization step makes the original query indistinguishable while making sure that the users always get the information that they need, there is no clear quantification of the privacy guarantee that the scheme offers over time.
\bigskip
\kat{Add here the comparison/contrast paragraph of microdata techniques shown previously, and your work}