From e02f02a894d11acfa05bebec56796e53cfd774e1 Mon Sep 17 00:00:00 2001 From: Manos Katsomallos Date: Sun, 18 Jul 2021 19:38:03 +0200 Subject: [PATCH] abstract: Review --- text/abstract.tex | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/text/abstract.tex b/text/abstract.tex index f09c1cb..a55aad5 100644 --- a/text/abstract.tex +++ b/text/abstract.tex @@ -18,8 +18,8 @@ We provide an insight into time-related properties of the algorithms, e.g.,~if t In the second part, we argue that in continuous data publishing, events are not equally significant in terms of privacy, and hence they should affect the privacy-preserving processing. Differential privacy is a well-established paradigm in privacy-preserving time series publishing. Different schemes exist, protecting either a single timestamp, or all the data per user or per window in the time series, considering however all timestamps as equally significant. -In this work, 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 design two privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets. +In this part, 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 design three privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets. \paragraph{Keywords:}