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Manos Katsomallos 2021-10-25 16:04:49 +02:00
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\chapter{Conclusion and future work} \chapter{Conclusion and future work}
\label{ch:con} \label{ch:con}
Continuous publishing of data, also known as time series, has found over the past decades several application domains, including healthcare, smart building, and traffic monitoring. Continuous publishing of data, also known as time series, has found over the past decades several application domains, including healthcare, smart building, and traffic monitoring.
In many cases, time series are geotagged data containing sensitive personal details, and thus their processing entails privacy concerns. In many cases, time series contain personal details (and are usually geotagged), and thus their processing entails privacy concerns.
The processing/publishing of time series that contain user-generated data, may not only pose privacy risks to the individuals involved but also deteriorate arbitrarily the data utility. The processing/publishing of user-generated data in the form of time series, may not only pose privacy risks to the individuals involved but also deteriorate arbitrarily the quality therein.
To this end, differential privacy is the most prominent privacy method that can efficiently balance between user protection and data utility. To this end, differential privacy is the most prominent privacy method that can efficiently balance between user protection and data utility.
In this thesis, we have concentrated on continuous user-generated data publishing. In this thesis, we have concentrated on continuous user-generated data publishing.