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.
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.
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.
We have studied the relevant literature with special emphasis on data correlation.
Furthermore, we explored ways to provide configurable protection in such settings and developed relevant solutions.
Next, we summarize this thesis in the individual chapters by describing our contribution to the problems surrounding quality and privacy in user-generated Big Data.
Subsequently, we discuss interesting perspectives and open questions for future investigation.
