correlation: Minor corrections

text/preliminaries/correlation.tex

@@ -1,8 +1,8 @@
 \section{Data correlation}
 \label{sec:correlation}
 
-
 \subsection{Types of correlation}
+\label{subsec:cor-types}
 
 The most prominent types of correlation might be:
 
@@ -18,6 +18,7 @@ A positive spatial autocorrelation indicates that similar data are \emph{cluster
 
 
 \subsection{Extraction of correlation}
+\label{subsec:cor-ext}
 
 A common practice for extracting data dependence from continuous data, is by expressing the data as a \emph{stochastic} or \emph{random process}.
 A random process is a collection of \emph{random variables} or \emph{bivariate data}, indexed by some set, e.g.,~a series of timestamps, a Cartesian plane $\mathbb{R}^2$, an $n$-dimensional Euclidean space, etc.~\cite{skorokhod2005basic}.
@@ -38,6 +39,7 @@ Some common stochastic processes modeling techniques include:
 
 
 \subsection{Privacy risks of correlation}
+\label{subsec:cor-prv}
 
 Correlation appears in dependent data:
 
@@ -60,6 +62,7 @@ A positive correlation indicates that the variables behave in a \emph{similar} m
 
 
 \subsection{Privacy loss under temporal correlation}
+\label{subsec:cor-temp}
 
 % The presence of temporal correlation might result into additional privacy loss consisting of \emph{backward privacy loss} $\alpha^B$ and \emph{forward privacy loss} $\alpha^F$~\cite{cao2017quantifying}.
 Cao et al.~\cite{cao2017quantifying} propose a method for computing the temporal privacy loss (TPL) of a differential privacy mechanism in the presence of temporal correlation and background knowledge.