Privacy Preserving Access of Outsourced Data in Heterogeneous Databases

Abstract

- Privacy is main concern in the world among present technological phase Information security has become a dangerous issue since the information sharing has a common need Recently privacy issues have been increased enormously when internet is flourishing with forums social media blogs and e-commerce etc Hence research area is retaining privacy in data mining The sensitive data of the data owners should not be known to the third parties and other data owners To make it efficient the horizontal partitioning is done on the heterogeneous databases is introduced to improve privacy and efficiency we address the major issues of privacy preservation in information mining In particular we consider to provide protection between different data owners and to give privacy between them by partitioning the databases horizontally and the data s are available in the heterogeneous databases Our proposed work is to center around the study of security saving on unknown databases and conceiving private refresh methods to database frameworks that backings thoughts of obscurity assorted than k-secrecy Symmetric homomorphic encryption scheme which is significantly more efficient than the asymmetric schemes Our proposed work helps the valid user can extract with key issue in partition data in automated approach and the data s are partitioned horizontally