Improving the Performance of Various Privacy Preserving Databases using Hybrid Geometric Data Perturbation Classification Model

Abstract

As the size of the privacy preserving databases is increasing, it is difficult to improve the privacy and accuracy of these databases due to dimensionality and runtime. However, most of the traditional privacy preserving models are independent of privacy and runtime. Also, it is essential to preserve the privacy of the large sensitive attributes before publishing it to the third-party servers. As a result, a novel framework is required to improve the privacy as well as accuracy on the high dimensional privacy preserving data with less runtime. In order to improve the privacy, accuracy and runtime of the traditional privacy preserving models, a hybrid perturbation based privacy preserving classification model is proposed on the multiple databases. In this work, a new data transformation approach, hybrid geometrical perturbation approach and hybrid boosting classifier are proposed in order to enhance the overall efficiency of the model on the privacy preserving databases. In this work, a hybrid geometric perturbation approach is used to enhance the privacy preserving on the sensitive attributes. Initially, a pre-processing method is applied on the input dataset in order to remove the noise in the feature values. A hybrid machine learning classifier is proposed to predict the privacy preserving class label based on the training data. Experimental results represents the proposed hybrid geometric perturbation based boosting classifier has better statistical accuracy, recall, precision and runtime than the conventional models.