In this section, we outline possible perspectives to this thesis regarding the different topics that we addressed.
\paragraph{Diversification of event categories}
% \kat{this is for me one of the clearest perspective so it should appear first in the list}
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.
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}.
Since we can easily adapt {\thething} privacy to the global processing and publishing scheme, it would be interesting to observe it in more diverse scenarios (including statistical data publishing) and develop suitable methodology.
% \kat{Hmm, i think that in the main text you say that even though the wok is for local differential privacy, nevertheless wlog you speak in terms of global differential privacy. In connection to that, this paragraph here is raising questions..}
% \kat{use another word, because this makes the current contribution sound 'simple'}
reasons of clarity
in order to facilitate a more straightforward definition of {\thething} privacy.
In the future,
% we plan to
fellow researchers can
explore more dynamic scenarios where data are processed and published in streaming mode, which will lead to adoption of time critical crowdsensing applications.
% \kat{no need to present all ideas as your future work. You can speak for fellow researchers, or research that can be done in general, following your current work. }
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}, i.e.,~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.
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.
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.
