abstract: Keywords

text/abstract.tex

@@ -29,4 +29,4 @@ The results of the experimental evaluation and comparative analysis of {\thethin
 
 
 \paragraph{Keywords:}
-information privacy, continuous data publishing, crowdsensing, privacy-preserving data processing
+data privacy, continuous data publishing, crowdsensing, privacy-preserving data processing

text/related/summary.tex

@@ -1,6 +1,19 @@
 \section{Summary}
 \label{sec:sum-rel}
+In this chapter, we surveyed the literature around the domain of privacy-preserving continuous data publishing.
+We offer a guide that would allow its users to choose the proper algorithm(s) for their specific use case.
+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{? Don't forget to mention here the publication that you have.}
+% \mk{Done in the beginning}
 
-In this chapter, we offer a guide that would allow its users to choose the proper algorithm(s) for their specific use case.
-
-\kat{? Don't forget to mention here the publication that you have.}
+Since the domain of data privacy is vast, several surveys have already been published with different scopes.
+A group of surveys focuses on specific different families of privacy-preserving algorithms and techniques.
+For instance, Simi et al.~\cite{simi2017extensive} provide an extensive study of works on $k$-anonymity and Dwork~\cite{dwork2008differential} focuses on differential privacy.
+Another group of surveys focuses on techniques that allow the execution of data mining or machine learning tasks with some privacy guarantees, e.g.,~Wang et al.~\cite{wang2009survey}, and Ji et al.~\cite{ji2014differential}.
+In a more general scope, Wang et al.~\cite{wang2010privacy} analyze the challenges of privacy-preserving data publishing, and offer a summary and evaluation of relevant techniques.
+Additional surveys look into issues around Big Data and user privacy.
+Indicatively, Jain et al.~\cite{jain2016big}, and Soria-Comas and Domingo-Ferrer~\cite{soria2016big} examine how Big Data conflict with pre-existing concepts of privacy-preserving data management, and how efficiently $k$-anonymity and $\varepsilon$-differential privacy deal with the characteristics of Big Data.
+Others narrow down their research to location privacy issues.
+To name a few, Chow and Mokbel~\cite{chow2011trajectory} investigate privacy protection in continuous LBSs and trajectory data publishing, Chatzikokolakis et al.~\cite{chatzikokolakis2017methods} review privacy issues around the usage of LBSs and relevant protection mechanisms and metrics, Primault et al.~\cite{primault2018long} summarize location privacy threats and privacy-preserving mechanisms, and Fiore et al.~\cite{fiore2019privacy} focus only on privacy-preserving publishing of trajectory microdata.
+Finally, there are some surveys on application-specific privacy challenges.
+For example, Zhou et al.~\cite{zhou2008brief} have a focus on social networks, and Christin et al.~\cite{christin2011survey} give an outline of how privacy aspects are addressed in crowdsensing applications.

text/resume.tex

@@ -22,4 +22,4 @@ Les résultats de l'évaluation expérimentale et de l'analyse comparative de la
 
 
 \paragraph{Mots clés :}
-confidentialité des informations, publication continue des données, crowdsensing, traitement des données préservant la confidentialité
+confidentialité des données, publication continue des données, crowdsensing, traitement des données préservant la confidentialité