Sensitive attribute based non-homogeneous anonymization for privacy preserving data mining

Data mining is the process of extracting interesting patterns or knowledge from large amount of data. With the development of data mining technology, an increasing number of data can be mined out to reveal some potential information about the user, because of which privacy of the user may be violated easily. Privacy Preserving Data Mining (PPDM) is used to mine the potential valuable knowledge without revealing the personal information of the individuals. K-Anonymity is one of the Privacy Preserving model that aims at making the individual record be indistinguishable among a group records by using techniques of generalization and suppression. The existing approaches are based on homogeneous anonymization that anonymizes quasi attributes by choosing a single sensitive attribute. This approach causes high information loss and reduces the data utility. To overcome these issues in the existing system, sensitive attribute based non-homogeneous anonymization system is proposed. Based on the sensitive attribute, non-homogeneous anonymization technique (generalization and suppression) is applied to the identified quasi attributes and the non sensitive attributes are directly published. Thus the proposed system achieves high degree of data utility, reduces information loss and also achieves high degree of Data Integrity.