Privacy preserving data mining has turned out to be progressively well
known on the grounds that it permits sharing of security delicate information for study
purposes. Nowadays, individuals have turned out to be progressively reluctant to share
their information, over and over again people are either declining to share their
information or giving erroneous information. As of late, protection safeguarding
information mining has been considered broadly, in light of the wide multiplication of
touchy data on the web. We examine strategy for randomization, k-anonymization, and
other security safeguarding information mining strategies. Learning is matchless
quality, and the more people are educated about data break-in, less inclined they will be
to fall prey to the underhanded programmer sharks of data innovation. In this paper, we
give a review of Privacy preserving data mining techniques.
Keywords: Condensation, Cryptography, Data mining, Perturbation, Privacy,
Privacy Preserving Data Mining.
