In today’s era acquiring information about others is not difficult task but securing this data form interlopers is a big deal. K-anonymity model used to protect released data. Released data which is available for public used may contain sensitive and non-sensitive data. But K-anonymity model faces changes when set of sensitive attributes are present in the data set. To achieve K-anonymous table with diversity may causes distortion of data in some extent. This paper proposed a new concept to minimize this data distortion without using tuple suppression for M-SA K-anonymity Model.

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K-anonymity Attacks on anonymous table l-diversity multiple sensitive attributes