A sigmoid based learning in heterogeneous distortion for data privacy

Abstract

Neural network-based learning models along with an access to huge data have made a remarkable outcome in recent years. These models are contributing a lot to improvise the working dimensions of various domains like Speech recognition, Image processing, Text analysis and many more. The well represented data is the main resource in the current research, but this data is often privacy sensitive and it definitely needs a proper attention failing which leads to serious privacy concerns. The proposed work demonstrates how learning models can be applied to analyze the data sensitivity and classify them to various privacy classes. Once the privacy class distribution is performed the model applies Inverse laplacian query model to check the data utility. The data should not get compromised on utility with the curse of privacy. With this intention the given experimental study succeeded in training the network to perform privacy analysis under a modest privacy budget, complexity training efficiency and data utility.