24 lines
2.8 KiB
TeX
24 lines
2.8 KiB
TeX
\chapter{Related work}
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\label{ch:rel}
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\kat{Be sure to update this chapter with more recent works, after the survey was published.. Moreover, the introduction here must be updated, we are not talking about a survey anymore but for your thesis. This means that possibly you need to add a section about some general privacy techniques, which go beyond the continuous publication scenario.}
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Since the domain of data privacy is vast, several surveys have already been published with different scopes.
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A group of surveys focuses on specific different families of privacy-preserving algorithms and techniques.
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For instance, Simi et al.~\cite{simi2017extensive} provide an extensive study of works on $k$-anonymity and Dwork~\cite{dwork2008differential} focuses on differential privacy.
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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}.
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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.
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Additional surveys look into issues around Big Data and user privacy.
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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.
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Others narrow down their research to location privacy issues.
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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.
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Finally, there are some surveys on application-specific privacy challenges.
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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.
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In the following sections, we document works that deal with privacy under continuous data publishing covering diverse use cases.
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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.
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\input{related/micro}
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\input{related/statistical}
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\input{related/summary}
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