Privacy-preserving data mining is the area of data mining that used to safeguard sensitive information from unsanctioned disclosure.The problem of privacy-preserving data mining has become more important in recent years because of the increasing ability to store personal data about users. A number of techniques such as randomization and k-anonymity ,bucketization,generlization have been proposed in recent years in order to perform privacy-preserving data mining. Forhigh-dimensiondata by using generalization significant amount of information is lost according to recent works. Whereas the Bucketization technique does not forbid membership and does not applicable to the data that does not have a clear distinction between sensitive attributes andquasi-identifyingattributesThus, this paper shows a solution to preserve privacy of high dimensional data
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