SMMCOA: Maintaining Multiple Correlations between Overlapped Attributes Using Slicing Technique

-Knowledge discovery is the most discussed topic now a day. Data which is collected from various resources are processed through various stages and the output of this process is the knowledge which is previously hidden. Basically data mining is a technique whose outputs are previously unknown and potentially useful information from data. There are several challenges of data mining are scalability, dimensionality, complex and heterogeneous data, data quality, data distribution and ownership, privacy preserving etc. This paper proposed a new model for privacy preserving data publishing named as Maintaining Multiple Correlations between Overlapped Attributes Using Slicing Technique. In slicing data is portioned in both way vertically and horizontally manner and Bucketization is applied in this resultant. This model is used to prevent against attribute correlation distortion. This paper shows the drawbacks of slicing and proposed a novel model to overcome these drawbacks, the proposed system generates uniform and efficient results even for overlapped attributes. Keywords-Privacy Preserving data Mining, Kanonymity, Slicing, Membership Disclosure, Attribute Correlations.