Introduction:
The term ’big data’ has become appearing these days. With the
universality of social networks, Internet of things (Iots) and
outsourced cloud computing, we have seen a great growth of data in
larger volume. Big data offers a great value and has become a driving
force in economic growth.
Natural Language Processing[1]
Application services and data business record user behaviors to improve
their business skills and to accomplish their purpose. Growing
techniques like social network, e-health system etc. offer excellent
utilities for better understanding. It is now obvious that when users
use these applications a lot of data points are formed and collected in
real time. Well it also means that sensitive data becomes breachable.
Privacy related problems occurs during data communication mining
processes and even when raw data are not present because these sensitive
data mining techniques can reveal private information.
With the improvement of privacy awareness, every institute is trying to
develop solution to accomplish the increasing requirements of privacy
safety. The goal is to make privacy safe so that people can work easily
without any privacy leakage. Most of the privacy technologies use
privacy preservation on
smaller scale. The task is to develop most efficient privacy preserving
big data analytics which can deal with any type of data. In the past,
communities have proposed many solutions.
From some past years many research groups take the challenge and create
approaches. And they still discuss and developing more findings and
clarify and summarize the existing approaches and contributions to
prevent the privacy.