An Ontology Based Prediction Process on Vertically Partitioned Data with Privacy Preservation

Abstract

Privacy preservation emphasize on authorization of data, which signi es that data should be accessed only by authorized users. The generalization of data with varying concept hierarchies seems to be interesting solution. This paper proposes two stage predic tion processes on privacy preserved data. The generalization with betraying is performed in rst stage to de ne the knowledge or hypothesis and which is further optimized using gradient descent method in second stage prediction process to obtain accurate results. The experiment carried with both batch and stochastic gradient methods and it is shown that bulk operation performed by batch takes more iterations than stochastic to give accurate solution