diff --git a/text/abstract.tex b/text/abstract.tex index 230b98d..e367984 100644 --- a/text/abstract.tex +++ b/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 diff --git a/text/related/summary.tex b/text/related/summary.tex index d6ca5a2..77712f9 100644 --- a/text/related/summary.tex +++ b/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. diff --git a/text/resume.tex b/text/resume.tex index 6330cf3..bf00a00 100644 --- a/text/resume.tex +++ b/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