Providing Privacy Using M-Diversity Algorithm along with K-Anonymity

Abstract: The data of any person can be divided into two categories. They are non-sensitive data and sensitive data. Coming to banking details, the bank�s database consists of large dataset regarding customers of the bank and their transactional details which are treated as most sensitive data. Any Intruder can get the sensitive data of the customer even the customer�s personal information is removed. Interlopers utilize a few information mining methods to recover the information and break down it and utilize the information for their own motivation. Gatecrashers can get the subtle elements of the managing an account clients through Quasi Identifier esteem which results to the homogeneity assault and membership disclosure assault. So to keep this information assaults this paper proposes new strategy which saves the security among the client's information through randomizing the client's information based on Quasi-Identifier.

Keywords: Anonymization, Regression, K-Anonymity, M-Diversity.