Efficient Machine Learning Model for Enhancing Privacy Preservation for Covid-19 Data

Abstract

Nowadays, the entire world is crying because of Covid-19 disease where it makes sudden death worldwide. The healthcare/medical industry take care of the public do save the people from Covid-19 by providing immediate and serious treatment. Since the whole world generating covid-19 data and it becomes a bigdata. The Covid-19 dataset includes personal, medical, financial, precision medicine and other data. Thus, any public, non-government organizations, insurance people can obtain the patient personal data. For example, approaching a covid patient for marketing to insurance policy conversion is a horrible task and it needs to be avoided. Also, people do not like to share their personal or medical information to any business or third-party people, and they need privacy preservation. Various earlier research works have proposed several methods for privacy preservation, but the accuracy is not up to the mark. This problem is considered as the major research problem and this paper motivated to design and implement a machine learning model for providing privacy preservation to the Covid-19 dataset. This paper mainly focusing on analyzing covid-19 dataset in terms of images. The proposed method implemented and experimented with MATLAB software and the results are verified. From the results, it is identified that the proposed model outperforms than the earlier approaches and illustrated in the experimental and results section.