From a1b3eda5d81c3563af272eba04a6b9448d3c8b5c Mon Sep 17 00:00:00 2001 From: Manos Katsomallos Date: Mon, 25 Oct 2021 17:48:02 +0200 Subject: [PATCH] summary: FInished the perspectives --- text/conclusion/perspectives.tex | 43 +++++++++++++++++++++++--------- 1 file changed, 31 insertions(+), 12 deletions(-) diff --git a/text/conclusion/perspectives.tex b/text/conclusion/perspectives.tex index 9af5c05..343068d 100644 --- a/text/conclusion/perspectives.tex +++ b/text/conclusion/perspectives.tex @@ -1,18 +1,37 @@ \section{Perspectives} \label{sec:persp} -\mk{WIP} -\paragraph{Global scheme} -statistical data - -\paragraph{Streaming mode} +\paragraph{Global {\thething} privacy} +For now, we have applied {\thething} privacy in the local scheme and for microdata due to the advantages of the local scheme over the global as we discussed in detail in Section~\ref{subsec:data-publishing}. +The adaptation of {\thething} privacy to support the global processing and publishing scheme would allow for the studying of more diverse scenarios including statistical data publishing. -\paragraph{Event categories} - -\paragraph{Spatiotemporal continuity} - -\paragraph{More correlations} +\paragraph{{\Thething} privacy over infinite event sequences} +So far, we considered for our problem setting finite time series that are processed in batch mode. +This was a decision that we made for the shake of simplicity in order to facilitate a more straightforward definition of {\thething} privacy. +In the future, we plan to explore more dynamic scenarios where data are processed and published in streaming mode, which will lead to adoption of time critical crowdsensing applications. -In the future, we aim to work on automatically learning the initial landmark set by analyzing the input data sets,semantics, and user preferences. -We also plan to introduce learning for the tuning of ourAdaptive scheme parameters. +\paragraph{{\Thething} privacy and spatiotemporal continuity} +In mereology, the formal study on the relation between parts and the entities they form, it is generally held that the identity of an observable object depends to its \emph{spatiotemporal continuity}~\cite{wiggins1967identity, scaltsas1981identity, hazarika2001qualitative}. +That is, the property of well-behaved objects that alter their state in harmony with space and time. +Considering events that span the entirety of the user-generated series of events thereof ensures the spatiotemporal continuity of the users. +This way, it is possible to acquire more information regarding individuals' identities, and thus design privacy schemes that offer improved privacy and utility guarantees. + + +\paragraph{Diversification of event categories} +With the proposal of {\thething} privacy we introduced {\thething} events in privacy-preserving continuous data publishing. +This categorization in regular and significant events enabled the development of a configurable differential privacy notion for time series. +Variation of the existing event categories, e.g.,~weighted {\thethings}, or the introduction of new ones, would allow for an even more fine-grained configuration of privacy protection and the development of variations of {\thething} privacy. + + +\paragraph{More data correlation types} +In the current state of our work, we consider {\thethings} as one-dimensional elements in our problem setting. +Consequently, we have explored {\thething} privacy under temporal correlation and examined the behavior of temporal privacy loss for different {\thething} percentages and distributions. +Accounting for other possible dimensions, e.g.,~location, can introduce more aspects to the current use case of {\thething} privacy. +Indicatively, as we have extensively studied in Section~\ref{sec:correlation}, there are many types of data correlation in time series to further research in the context of {\thething} privacy. + + +\paragraph{Incorporation of machine learning} +Until now, we consider the {\thething} discovery and selection process orthogonal to our work. +In the future, we aim to work on automatically learning the initial {\thething} set by analyzing the input data sets, semantics, and user preferences. +We also plan to introduce learning for the tuning of our \texttt{Adaptive} scheme parameters, which will further improve its sampling component and overall utility performance.