diff --git a/text/bibliography.bib b/text/bibliography.bib index 50db20d..314073f 100644 --- a/text/bibliography.bib +++ b/text/bibliography.bib @@ -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, diff --git a/text/conclusion/summary.tex b/text/conclusion/summary.tex index f5af63a..2d46e96 100644 --- a/text/conclusion/summary.tex +++ b/text/conclusion/summary.tex @@ -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}. diff --git a/text/evaluation/main.tex b/text/evaluation/main.tex index 7936483..b0336e4 100644 --- a/text/evaluation/main.tex +++ b/text/evaluation/main.tex @@ -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. diff --git a/text/introduction/contribution.tex b/text/introduction/contribution.tex index 58d6dfa..d545ab2 100644 --- a/text/introduction/contribution.tex +++ b/text/introduction/contribution.tex @@ -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. diff --git a/text/problem/main.tex b/text/problem/main.tex index 5734ce3..4311bf9 100644 --- a/text/problem/main.tex +++ b/text/problem/main.tex @@ -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.