Respite for Customer’s Privacy Issues using Privacy Preserving Data Mining

Abstract

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.

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