19 lines
1.3 KiB
TeX
19 lines
1.3 KiB
TeX
\chapter{Conclusion and future work}
|
|
\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.
|
|
In many cases, time series contain personal details (and are usually geotagged), and thus their processing entails privacy concerns.
|
|
|
|
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.
|
|
|
|
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.
|
|
|
|
|
|
\input{conclusion/summary}
|
|
\input{conclusion/perspectives}
|