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katsomallos2022landmark: Updated the status

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Manos Katsomallos 2022-01-07 01:09:19 +01:00
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text/bibliography.bib
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@ -995,7 +995,7 @@
author = {Katsomallos, Manos and Tzompanaki, Katerina and Kotzinos, Dimitris},
booktitle = {Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy},
year = {2022},
note = {Under review}
note = {To appear}
}
@inproceedings{kellaris2013practical,

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text/conclusion/summary.tex
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@ -31,4 +31,4 @@ We presented ($\varepsilon$, $L$)-\emph{{\thething} privacy}, a novel privacy no
We showed that our methodology can provide adequate differential privacy guarantees while achieving better data utility than the user-level scheme.
\end{itemize}
% \kat{mention here again that the work appears in the article... submitted at...}
This work is under review for being published in the proceedings of the $12$th ACM conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.
This work will appear in the proceedings of the $12$th ACM conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.

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text/evaluation/main.tex
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@ -1,6 +1,6 @@
\chapter{Evaluation}
\label{ch:eval}
\nnfootnote{This chapter is under review for being published in the proceedings of the $12$th ACM conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.\bigskip}
\nnfootnote{This chapter will appear in the proceedings of the $12$th ACM conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.\bigskip}
In this chapter, we present the experiments that we performed in order to evaluate {\thething} privacy (Chapter~\ref{ch:lmdk-prv}) on real and synthetic data sets.
Section~\ref{sec:eval-dtl} contains all the details regarding the data sets the we used for our experiments along with the system configurations.

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text/introduction/contribution.tex
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@ -44,6 +44,6 @@ Furthermore, we estimate the impact of the privacy-preserving dummy {\thething}
The second and the third contributions are described in the article~\cite{katsomallos2022landmark},
% \kat{cite the technical report}
which is submitted at the research papers track
which will appear at the research papers track
% \kat{name the conference}
of the $12$th ACM conference on Data and Application Security and Privacy.

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text/problem/main.tex
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@ -1,6 +1,6 @@
\chapter{{\Thething} privacy}
\label{ch:lmdk-prv}
\nnfootnote{This chapter is under review for being published in the proceedings of the $12$th ACM conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.}
\nnfootnote{This chapter will appear in the proceedings of the $12$th ACM conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.}
% 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.