From d40de1b4d8cc9c514fa0f7b62f22f88a89728914 Mon Sep 17 00:00:00 2001
From: Manos Katsomallos <manos@delkappa.com>
Date: Sun, 10 Oct 2021 19:47:16 +0200
Subject: [PATCH] problem: Added intro for landmark privacy

---
 text/problem/main.tex | 37 ++++++++++++++++++++++++++++++++++++-
 1 file changed, 36 insertions(+), 1 deletion(-)

diff --git a/text/problem/main.tex b/text/problem/main.tex
index b352e6e..2b90772 100644
--- a/text/problem/main.tex
+++ b/text/problem/main.tex
@@ -1,8 +1,43 @@
 \chapter{Landmark Privacy}
 \label{ch:lmdk-prv}
 
+% Crowdsensing applications
+The plethora of sensors currently embedded in personal devices and other infrastructures have paved the way for the development of numerous \emph{crowdsensing services} (e.g.,~Ring~\cite{ring}, TousAntiCovid~\cite{tousanticovid}, Waze~\cite{waze}, etc.) based on the collected personal, and usually geotagged and timestamped data.
+% Continuously user-generated data
+User--service interactions gather personal event-like data, that are data items comprised of pairs of an identifying attribute of an individual and the---possibly sensitive---information at a timestamp (including contextual information), e.g.,~(\emph{`Bob', `dining', `Canal Saint-Martin', $17{:}00$}).
+When the interactions are performed in a continuous manner, we obtain ~\emph{time series} of events.
+% Observation/interaction duration
+Depending on the duration, we distinguish the interaction/observation into \emph{finite}, when taking place during a predefined time interval, and \emph{infinite}, when taking place in an uninterrupted fashion.
+Example~\ref{ex:scenario} shows the result of user--LBS interaction while retrieving location-based information or reporting user-state at various locations.
+ 
+\begin{example}
+  \label{ex:scenario}
+
+  Consider a finite sequence of spatiotemporal data generated by Bob during an interval of $8$ timestamps, as shown in Figure~\ref{fig:scenario}.
+  Events in a shade correspond to privacy-sensitive events that Bob has defined beforehand. For instance his home is around {\'E}lys{\'e}e, his workplace is around the Louvre, and his hangout is around Canal Saint-Martin.
+
+  \begin{figure}[htp]
+    \centering
+    \includegraphics[width=\linewidth]{problem/lmdk-scenario}
+    \caption{A time series with {\thethings} (highlighted in gray).
+    }
+    \label{fig:scenario}
+  \end{figure}
+
+\end{example}
+
+% Privacy-preserving data processing
+The services collect and further process the time series in order to give useful feedback to the involved users or to provide valuable insight to various internal/external analytical services.
+The regulation regarding the processing of user-generated data sets~\cite{tankard2016gdpr} requires the provision of privacy guarantees to the users. 
+At the same time, it is essential to provide utility metrics to the final consumers of the privacy-preserving process output. 
+To accomplish this, various privacy techniques perturb the original data or the processing output at the expense of the overall utility of the final output.
+A widely recognized tool that introduces probabilistic randomness to the original data, while quantifying with a parameter $\varepsilon$ (`privacy budget'~\cite{mcsherry2009privacy}) the privacy/utility ratio is \emph{$\varepsilon$-differential privacy}~\cite{dwork2006calibrating}.
+Due to its \emph{composition} property, i.e.,~the combination of differentially private outputs satisfies differential privacy as well, differential privacy is suitable for privacy-preserving time series publishing.
+\emph{Event}, \emph{user}~\cite{dwork2010differential, dwork2010pan}, and \emph{$w$-event}~\cite{kellaris2014differentially} comprise the possible levels of privacy protection.
+Event-level limits the privacy protection to \emph{any single event}, user-level protects \emph{all the events} of any user, and $w$-event provides privacy protection to \emph{any sequence of $w$ events}.
+
 In this chapter, we propose a novel configurable privacy scheme, \emph{\thething} privacy, which takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly.
-We propose two privacy models that guarantee {\thething} privacy.
+We propose three privacy models that guarantee {\thething} privacy.
 To further enhance our privacy method, and protect the {\thethings} position in the time series, we propose techniques to perturb the initial {\thethings} set (Section~\ref{sec:theotherthing}).
 
 \input{problem/thething/main}