Distinctive Context Sensitive and Hellinger Convolutional Learning for Privacy Preserving of Big Healthcare Data

Abstract

The collection and effectiveness of sensitive Big Data have grown with Information Technology (IT) development. While using sensitive Big Data to acquire relevant information, it becomes indispensable that irrelevant sensitive data are reduced to safeguard personal information in healthcare sector. Many privacy-preserving strategies have been applied in the recent years using quasi-identifiers (QI) for applications like health services. However, privacy preservation over quasi-identifiers is still challenging in the context of Big Data because most datasets were of huge volume. Existing methods suffer from higher time consumption and lower data utility because of dynamically progressing datasets. In this paper, an efficient Distinctive Context Sensitive and Hellinger Convolutional Learning (DCS-HCL) is introduced to ensure privacy preservation and achieve high data utility for big healthcare datasets. First, Distinctive Impact Context Sensitive Hashing model is designed for the given input Big Dataset where both the distinctive and impact values are identified and applied to Context Sensitive Hashing. With this, similar QI-classes are mapped to evolve the computationally efficient anonymyzed data. Second, Hellinger Convolutional Neural Privacy Preservation model is presented to preserve the privacy of the sensitive unstructured data. This is performed by hashing QI-class values, weight updation and bias in CNN to increase the accuracy and to reduce the information loss. Evaluation results demonstrate that with proposed method with large-volume unstructured datasets improved performance of run time, data utility, information loss and accuracy significantly over existing methods.