\chapter{Related work}
\label{ch:rel}

\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.}

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.

In the following sections, we document works that deal with privacy under continuous data publishing covering diverse use cases.
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.

\input{related/micro}
\input{related/statistical}
\input{related/summary}
