  Data Mining is a way of extracting data or uncovering hidden patterns of
information from databases. So, there is a need to prevent the inference rules
from being disclosed such that the more secure data sets cannot be identified
from non sensitive attributes. This can be done through removing or adding
certain item sets in the transactions Sanitization. The purpose is to hide the
Inference rules, so that the user may not be able to discover any valuable
information from other non sensitive data and any organisation can release all
samples of their data without the fear of Knowledge Discovery In Databases
which can be achieved by investigating frequently occurring item sets, rules
that can be mined from them with the objective of hiding them. Another way is
to release only limited samples in the new database so that there is no
information loss and it also satisfies the legitimate needs of the users. The
major problem is uncovering hidden patterns, which causes a threat to the
database security. Sensitive data are inferred from non-sensitive data based on
the semantics of the application the user has, commonly known as the inference
problem. Two fundamental approaches to protect sensitive rules from disclosure
are that, preventing rules from being generated by hiding the frequent sets of
data items and reducing the importance of the rules by setting their confidence
below a user-specified threshold.
