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.}
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.
