Privacy Preserving in DM using min-max normalization and noise addition

Abstract

Privacy preserving allows sharing of privacy sensitive data for analysis purposes so it is very popular
technique. so, people have ready to share their data. In recent years, privacy preserving data mining is an important one
because wide availability of data is there. It is used for protecting the privacy of the critical and sensitive data and
obtains more accurate results of data mining. The random noise is added to the original data in privacy preserving data
Mining (PPDM) approach, which is used to publish the accurate information about original data. The main objective of
privacy preserving data mining is to develop algorithms for modifying the original data and securing the information to
be misused, so that the private data and private k nowledge remain as it is after mining process. This topic is used to
reiterate several privacy preserving data mining technologies to protect sensitive information’s privacy and obtaining
data clustering with minimum information loss for multiplicative attributes in dataset.