Two Stage Prediction Process with Gradient Descent Methods Aligning with the Data Privacy Preservation

Abstract

Privacy preservation emphasize on authorization of data, which signifies that data should be accessed only by authorized users. Ensuring the privacy of data is considered as one of the challenging task in data management. 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 preserved using generalization and betraying other communicating parties by disguising generalized data which adds another level of privacy. The generalization with betraying is performed in first stage to define the knowledge or hypothesis and which is further optimized using gradient descent method in second stage prediction for accurate prediction of data. The experiment carried with both batch and stochastic gradient methods and it is shown that bulk operation performed by batch takes long time and more iterations than stochastic to give more accurate solution.