Conclusion:
In this paper, we have surveyed the latest establishment in the field of
privacy-preserving big data analytics. We have provided an extensive and
structured reportage of collective big data analytics and
privacy-preserving strategy before introducing a novel taxonomy of
privacy-preserving big data analytics integrate with inspiring privacy
scenarios in the big data factor. We explicitly focus on three main
privacy-preserving data analytics problems including data publishing,
data querying, and data mining. The proposed taxonomy is anticipate to
give a systematic and multi-dimensional view of emerging topics in the
field. In spite of recent years both industry and academia have put a
lot of efforts into privacy-preserving big data analytics, this field is
still indisputable demanding and it leaves a room for improving the
existing schemes as well as developing new novel approaches with regard
to an increase of performance and privacy level.
The generality of issue data sources and outsourced computing, the
development of machine learning as service, collaborative learning have
prompted cutting-edge solutions for privacy-related issues in the
future.