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graphics/table-related.tex
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{
\setlength\tabcolsep{2pt}
\fontsize{5.35}{7.5}\selectfont
\begin{longtabu} [c]{@{} *{9}l @{}}
\toprule
\multirow{2}{*}[-2pt]{\textbf{Name}} & \multicolumn{3}{c}{\textbf{Data}} & \multicolumn{4}{c}{\textbf{Protection}} & \multirow{2}{*}[-2pt]{\textbf{Correlations}} \\ \cmidrule(l{2pt}r{2pt}){2-4} \cmidrule(l{2pt}r{2pt}){5-8}
& \textbf{Input/Output} & \textbf{Processing} & \textbf{Publishing} & \textbf{Attack} & \textbf{Method} & \textbf{Level} & \textbf{Distortion} & \\ \midrule \endhead
\multicolumn{9}{c}{\textbf{Microdata}} \\ \midrule
\hyperlink{he2011preventing}{\emph{$e-$equivalence}}~\cite{he2011preventing} & stream & batch & sequential & complementary & $l$-diversity & event & generalization & - \\
& & & & release & & & & \\ \tabucline[hdashline]{-}
\hyperlink{li2016hybrid}{Li et al.}~\cite{li2016hybrid} & stream & batch & sequential & complementary & $l$-diversity & event & generalization, & - \\
& & & & release & & & perturbation & \\ \tabucline[hdashline]{-}
\hyperlink{zhou2009continuous}{Zhou et al.}~\cite{zhou2009continuous} & stream & streaming & continuous & same with & $k$-anonymity & event & generalization, & - \\
& & & & $k$-anonymity~\cite{sweeney2002k} & & & randomization & \\ \tabucline[hdashline]{-}
\hyperlink{gotz2012maskit}{\textbf{\emph{MaskIt}}}~\cite{gotz2012maskit} & stream & streaming & continuous & correlations & $\delta$-privacy & user & suppression & temporal \\
& & & & & & & & (Markov) \\ \tabucline[hdashline]{-}
\hyperlink{ma2017plp}{\textbf{\emph{PLP}}}~\cite{ma2017plp} & stream & streaming & continuous & correlations & $\delta$-privacy & user & suppression & spatiotem- \\
& & & & & & & (probabilistic) & poral (CRFs) \\
\midrule
\hyperlink{wang2006anonymizing}{\emph{$(X, Y)-$}} & sequential & batch & sequential & complementary & $k$-anonymity & event & generalization & - \\
\hyperlink{wang2006anonymizing}{\emph{privacy}}~\cite{wang2006anonymizing} & & & & release & & & & \\ \tabucline[hdashline]{-}
\hyperlink{Shmueli}{Shmueli and} & sequential & batch & sequential & same with & $l$-diversity & event & generalization & - \\
\hyperlink{Shmueli}{Tassa}~\cite{shmueli2015privacy} & & & & $l$-diversity~\cite{machanavajjhala2006diversity} & & & & \\ \tabucline[hdashline]{-}
\hyperlink{xiao2007m}{\emph{$m-$invariance}}~\cite{xiao2007m} & sequential & batch & sequential & complementary & $l$-diversity & event & generalization & - \\
& & & & release & & & & \\ \tabucline[hdashline]{-}
\hyperlink{chen2011differentially}{\textbf{Chen et al.}}~\cite{chen2011differentially} & sequential & batch & one-shot & linkage & differential & user & perturbation & - \\
& & & & & privacy & & (Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{jiang2013publishing}{\textbf{Jiang et al.}}~\cite{jiang2013publishing} & sequential & batch & one-shot & linkage & differential & user & perturbation & - \\
& & & & & privacy & & (Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{fung2008anonymity}{\emph{$BCF-$}} & sequential & batch & incremental & complementary & $k$-anonymity & event & generalization & - \\
\hyperlink{fung2008anonymity}{\emph{anonymity}}~\cite{fung2008anonymity} & & & & release & & & & \\ \tabucline[hdashline]{-}
\hyperlink{xiao2015protecting}{\textbf{Xiao et al.}}~\cite{xiao2015protecting} & sequential & streaming & sequential & correlations & $\delta$-location set & user & \emph{Planar Isotropic} & temporal \\
& & & & & & & \emph{Mechanism (PIM)} & (Markov) \\ \tabucline[hdashline]{-}
\hyperlink{al2018adaptive}{\textbf{Al-Dhubhani et al.}}~\cite{al2018adaptive} & sequential & streaming & sequential & correlations & geo-indistin- & user & perturbation & temporal \\
& & & & & guishability & & (Planar Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{ghinita2009preventing}{\textbf{Ghinita et al.}}~\cite{ghinita2009preventing} & sequential & streaming & sequential & linkage & spatiotemporal & user & generalization (spatio- & feature \\
& & & & & transformation & & temporal cloaking) & \\
\midrule
\hyperlink{primault2015time}{\textbf{\emph{Promesse}}}~\cite{primault2015time} & time series & batch & one-shot & spatiotemporal & temporal & user & perturbation & - \\
& & & & inference & transformation & & (temporal) & \\ \midrule
\multicolumn{9}{c}{\textbf{Statistical Data}} \\ \midrule
\hyperlink{chan2011private}{Chan et al.}~\cite{chan2011private} & stream/ & streaming & continual & linkage & differential & event & perturbation & - \\
& continual & & & & privacy & & (Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{cao2015differentially}{\textbf{\emph{l-trajectory}}}~\cite{cao2015differentially} & stream/ & streaming & continuous & linkage & $l-$trajectory & w-event & perturbation & - \\
& time series & & & & & personalized & (dynamic Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{bolot2013private}{Bolot et al.}~\cite{bolot2013private} & stream & streaming & continual & linkage & differential & event & perturbation & - \\
& & & & & privacy & & (Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{quoc2017privapprox}{\emph{PrivApprox}}~\cite{quoc2017privapprox} & stream & streaming & continual & linkage & zero & event & perturbation (ran- & - \\
& & & & & knowledge & & domized response) & \\ \tabucline[hdashline]{-}
\hyperlink{li2007hiding}{Li et al.}~\cite{li2007hiding} & stream & streaming & continuous & linkage & randomization & event & perturbation & serial \\
& & & & & & & (dynamic) & (data trends) \\ \tabucline[hdashline]{-}
\hyperlink{chen2017pegasus}{\emph{PeGaSus}}~\cite{chen2017pegasus} & stream & streaming & continuous & linkage & differential & event & perturbation & - \\
& & & & & privacy & & (Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{cao2017quantifying}{Cao et al.}~\cite{cao2017quantifying} & stream & streaming & continuous & correlations & differential & event & perturbation & temporal \\
& & & & & privacy & & (Laplace) & (Markov) \\ \tabucline[hdashline]{-}
\hyperlink{kellaris2014differentially}{Kellaris et al.}~\cite{kellaris2014differentially} & stream & streaming & continuous & linkage & differential & w-event & perturbation & - \\
& & & & & privacy & & (dynamic Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{wang2016rescuedp}{\textbf{\emph{RescueDP}}}~\cite{wang2016rescuedp} & stream & streaming & continuous & linkage & differential & w-event & perturbation & serial \\
& & & & & privacy & & (dynamic Laplace) & (Pearson's r) \\
\midrule
\hyperlink{kellaris2013practical}{Kellaris et al.}~\cite{kellaris2013practical} & sequential & batch & one-shot & linkage & differential & event & perturbation & - \\
& & & & & privacy & & (Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{chen2012differentially}{\textbf{Chen et al.}}~\cite{chen2012differentially} & sequential & batch & one-shot & linkage & differential & user & perturbation & - \\
& & & & & privacy & & (adaptive Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{hua2015differentially}{\textbf{Hua et al.}}~\cite{hua2015differentially} & sequential & batch & one-shot & linkage & differential & user & perturbation (ex- & - \\
& & & & & privacy & & ponential, Laplace) & \\ \tabucline[hdashline]{-}
\hyperlink{li2017achieving}{\textbf{Li et al.}}~\cite{li2017achieving} & sequential & batch & one-shot & linkage & differential & user & perturbation & - \\
& & & & & privacy & & (Laplace) & \\
\midrule
\hyperlink{erdogdu2015privacy}{Erdogdu et al.}~\cite{erdogdu2015privacy} & time series & batch/ & continual & correlations & $\epsilon_t$-privacy & user & perturbation & serial \\
& & streaming & & & & & (stohastic) & (HMM) \\ \tabucline[hdashline]{-}
\hyperlink{yang2015bayesian}{\emph{Bayesian differen-}} & time series & batch & one-shot & correlations & \emph{Pufferfish} & event & perturbation & general \\
\hyperlink{yang2015bayesian}{\emph{tial privacy}}~\cite{yang2015bayesian} & & & & & & & (Laplace) & (Gaussian) \\ \tabucline[hdashline]{-}
\hyperlink{song2017pufferfish}{Song et al.}~\cite{song2017pufferfish} & time series & batch & one-shot & correlations & \emph{Pufferfish} & event/user & perturbation & general \\
& & & & & & & (dynamic Laplace) & (Markov) \\ \tabucline[hdashline]{-}
\hyperlink{fan2013differentially}{\textbf{Fan et al.}}~\cite{fan2013differentially} & time series & streaming & continuous & correlations & differential & event & perturbation & spatiotem- \\
& & & & & privacy & & (Laplace) & poral/serial \\ \tabucline[hdashline]{-}
\hyperlink{wang2017cts}{\emph{CTS-DP}}~\cite{wang2017cts} & time series & streaming & continuous & correlations & differential & event & perturbation & serial (autocor- \\
& & & & & privacy & & \emph{(correlated Laplace)} & relation function) \\ \tabucline[hdashline]{-}
\hyperlink{fan2014adaptive}{\emph{FAST}}~\cite{fan2014adaptive} & time series & streaming & continuous & linkage & differential & user & perturbation & - \\
& & & & & privacy & & (dynamic Laplace) & \\
\bottomrule
\caption{Summary table of reviewed privacy methods. Location specific techniques are listed in bold, the rest are not data-type specific.}
\label{tab:related}
\end{longtabu}
}

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related.tex
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\chapter{Related work} \chapter{Related work}
\label{ch:rel} \label{ch:rel}
This is the related work. 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.
Nevertheless, to the best of our knowledge, there is no up-to-date survey that deals with privacy under continuous data publishing covering diverse use cases.
Such a survey 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{micro}
\input{statistical}
\section{Summary} \section{Summary}