From f6b2b32a57a8eb9963146b0cb8a297577d99d38d Mon Sep 17 00:00:00 2001 From: Manos Katsomallos Date: Mon, 2 Aug 2021 23:15:56 +0300 Subject: [PATCH] correlation: Minor corrections --- text/preliminaries/correlation.tex | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/text/preliminaries/correlation.tex b/text/preliminaries/correlation.tex index 92bfe01..7ccad56 100644 --- a/text/preliminaries/correlation.tex +++ b/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.