Two Stage Prediction Process with Gradient Descent Methods Aligning with the Data Privacy Preservation
Privacy preservation emphasize on authorization of data, which signiﬁes that data should be accessed onlyby authorized users. Ensuring the privacy of data is considered as one of the challenging task in datamanagement. The generalization of data with varying concept hierarchies seems to be interesting solution.This paper proposes two stage prediction processes on privacy preserved data. The privacy is preservedusing generalization and betraying other communicating parties by disguising generalized data which addsanother level of privacy. The generalization with betraying is performed in ﬁrst stage to deﬁne the knowledgeor hypothesis and which is further optimized using gradient descent method in second stage prediction foraccurate prediction of data. The experiment is carried with both batch and stochastic gradient methodsand it is shown that bulk operation performed by batch takes long time and more iterations than stochasticto give more accurate solution.Keywords : Batch Gradient, Gradient Descent, RDF and Ontology, Stochastic Gradient.