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Manos Katsomallos
2021-07-27 09:19:09 +03:00
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text/preliminaries/privacy.tex
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\section{Privacy}
\section{Data privacy}
\label{sec:privacy}
When personal data are publicly released, either as microdata or statistical data, individuals' privacy can be compromised, i.e,~an adversary becomes certain about an individual's personal information with a probability higher than a desired threshold.
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Attribute disclosure appears when it is revealed from (a privacy-protected version of) the microdata of Table~\ref{tab:snapshot-micro} that Quackmore is $62$ years old.
\subsection{Levels}
\subsection{Levels of privacy protection}
\label{subsec:prv-levels}
The information disclosure that a data release may entail is linked to the protection level that indicates \emph{what} a privacy-preserving algorithm is trying to achieve.
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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.
\subsection{Attacks}
\subsection{Attacks to privacy}
\label{subsec:prv-attacks}
Information disclosure is typically achieved by combining supplementary (background) knowledge with the released data or by setting unrealistic assumptions while designing the privacy-preserving algorithms.
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In order to better protect the privacy of Donald in case of attacks, the data should be privacy-protected in a more adequate way (than without the attacks).
\subsection{Operations}
\subsection{Privacy-preserving operations}
\label{subsec:prv-operations}
Protecting private information, which is known by many names (obfuscation, cloaking, anonymization, etc.), is achieved by using a specific basic privacy protection operation.
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For these reasons, there will be no further discussion around this family of techniques in this article.
\subsection{Seminal works}
\subsection{Seminal works in privacy protection}
\label{subsec:prv-seminal}
For completeness, in this section we present the seminal works for privacy-preserving data publishing, which, even though originally designed for the snapshot publishing scenario, have paved the way, since many of the works in privacy-preserving continuous publishing are based on or extend them.