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 \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.