From 10880daa0fea495987001f8c570e86e0f9ac5a48 Mon Sep 17 00:00:00 2001 From: katerinatzo Date: Tue, 12 Oct 2021 23:40:25 +0200 Subject: [PATCH] 5.4 --- text/evaluation/summary.tex | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/text/evaluation/summary.tex b/text/evaluation/summary.tex index 777b066..c4470b2 100644 --- a/text/evaluation/summary.tex +++ b/text/evaluation/summary.tex @@ -1,8 +1,9 @@ \section{Summary} \label{sec:eval-sum} -In this chapter we presented the experimental evaluation of the {\thething} privacy mechanisms and privacy-preserving {\thething} selection mechanism that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets. +In this chapter we presented the experimental evaluation of the {\thething} privacy mechanisms and the privacy-preserving {\thething} selection mechanism that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets. The Adaptive mechanism is the most reliable and best performing mechanism, in terms of overall data utility, with minimal tuning across most cases. -Skip performs optimally in data sets with a lower value range where approximation fits best. -The {\thething} selection component introduces a reasonable data utility decline to all of our mechanisms however, the Adaptive handles it well and bounds the data utility to higher levels compared to user-level protection. +Skip performs optimally in data sets with a smaller target value range, where approximation fits best. +The {\thething} selection component introduces a reasonable data utility decline to all of our mechanisms however, the Adaptive handles it well and bounds the data utility to higher levels compared to user-level protection.\kat{it would be nice to see it clearly on Figure 5.5. (eg, by including another bar that shows adaptive without landmark selection)} In terms of temporal correlation, we observe that under moderate and strong temporal correlation, a greater average regular--{\thething} event distance in a {\thething} distribution causes greater overall privacy loss. +Finally, the contribution of the {\thething} privacy on enhancing the data quality, while preserving $\epsilon$-differential privacy is demonstrated by the fact that the selected, Adaptive mechanism provides better data quality than the user-level mechanism.