From 45c6358a16116aaa9baf7453714d84c043690824 Mon Sep 17 00:00:00 2001 From: katerinatzo Date: Mon, 9 Aug 2021 16:41:30 +0300 Subject: [PATCH] 2.2.3. comments katerina --- text/preliminaries/privacy.tex | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/text/preliminaries/privacy.tex b/text/preliminaries/privacy.tex index 445ab79..c7a15d2 100644 --- a/text/preliminaries/privacy.tex +++ b/text/preliminaries/privacy.tex @@ -61,15 +61,16 @@ Users are subject to privacy attacks, and thus are the main point of interest of In more detail, the privacy protection levels are: \begin{enumerate}[(a)] - \item \emph{Event}~\cite{dwork2010differential, dwork2010pan}---limits the privacy protection to \emph{any single event} in a time series, providing maximum data utility. + \item \emph{Event}~\cite{dwork2010differential, dwork2010pan}---limits the privacy protection to \emph{any single event} in a time series, providing maximum \kat{maximum? better say high} data utility. \item \emph{$w$-event}~\cite{kellaris2014differentially}---provides privacy protection to \emph{any sequence of $w$ events} in a time series. - \item \emph{User}~\cite{dwork2010differential, dwork2010pan}---protects \emph{all the events} in a time series, providing maximum privacy protection. + \item \emph{User}~\cite{dwork2010differential, dwork2010pan}---protects \emph{all the events} in a time series, providing maximum\kat{maximum? better say high} privacy protection. \end{enumerate} Figure~\ref{fig:prv-levels} demonstrates the application of the possible protection levels on the statistical data of Example~\ref{ex:continuous}. For instance, in event-level (Figure~\ref{fig:level-event}) it is hard to determine whether Quackmore was dining at Opera at $t_1$. Moreover, in user-level (Figure~\ref{fig:level-user}) it is hard to determine whether Quackmore was ever included in the released series of events at all. Finally, in $2$-event-level (Figure~\ref{fig:level-w-event}) it is hard to determine whether Quackmore was ever included in the released series of events between the timestamps $t_1$ and $t_2$, $t_2$ and $t_3$, etc. (i.e.,~for a window $w = 2$). +\kat{Already, by looking at the original counts, for the reader it is hard to see if Quackmore was in the event/database. So, we don't really get the difference among the different levels here.} \begin{figure}[htp] \centering @@ -82,15 +83,15 @@ Finally, in $2$-event-level (Figure~\ref{fig:level-w-event}) it is hard to deter \subcaptionbox{$2$-event-level\label{fig:level-w-event}}{% \includegraphics[width=.32\linewidth]{level-w-event}% }\hspace{\fill} - \caption{Protecting the data of Table~\ref{tab:continuous-statistical} on (a)~event-, (b)~user-, and (c)~$2$-event-level. A suitable distortion method can be applied accordingly.} + \caption{Protecting the data of Table~\ref{tab:continuous-statistical} on (a)~event-, (b)~user-, and (c)~$2$-event-level. A suitable distortion method can be applied accordingly. \kat{Why don't you distort the results already in this table?}} \label{fig:prv-levels} \end{figure} -Contrary to event-level, that provides privacy guarantees for a single event, user- and $w$-event-level offer stronger privacy protection by protecting a series of events. +Contrary to event-level, which provides privacy guarantees for a single event, user- and $w$-event-level offer stronger privacy protection by protecting a series of events. Event- and $w$-event-level handle better scenarios of infinite data observation, whereas user-level is more appropriate when the span of data observation is finite. $w$-event- is narrower than user-level protection due to its sliding window processing methodology. -In the extreme cases where $w$ is equal to either $1$ or to the size of the entire length of the time series, $w$-event- matches event- or user-level protection, respectively. -Although the described levels have been coined in the context of \emph{differential privacy}~\cite{dwork2006calibrating}, a seminal privacy method that we will discuss in more detail in Section~\ref{subsec:prv-statistical}, it is possible to apply their definitions to other privacy protection techniques as well. +In the extreme cases where $w$ is equal either to $1$ or to the length of the time series, $w$-event- matches event- or user-level protection, respectively. +Although the described levels have been coined in the context of \emph{differential privacy}~\cite{dwork2006calibrating}, a seminal privacy method that we will discuss in more detail in Section~\ref{subsec:prv-statistical}, they are also used for other privacy protection techniques as well. \subsection{Privacy-preserving operations}