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.
%The processing/publishing of user-generated data in the form of time series, poses privacy risks to the individuals involved.\kat{the deterioration of the quality that we deal with here, is because of the privacy protection of the data, not because of the processing/publishing. I rephrased, together with the next sentence.}
Several methods have been proposed in order to protect the privacy of individuals while processing their data, but cannot avoid to deteriorate arbitrarily the quality therein. Out of these methods, we distinguish differential privacy, which quantifies the balance between user protection and data utility by a factor $\varepsilon$.
In this thesis, we have concentrated on continuous user-generated data publishing.
% \kat{say exactly what case you covered, this is way to general. and connect it to the differential privacy that you previously mentioned, otherwise it seems irrelevant.}
Particularly, we focused on providing differential privacy over finite time series while accounting for privacy-significant future and past events.
