Machine Learning based Encryption Framework for Privacy Preservation in Covid 19 Data

Machine Learning based Encryption Framework for Privacy Preservation in Covid 19 Data

© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : R. Karunia krishnapriya, G. Vinodhini, R. Suban, K. Sakthivel
DOI : 10.14445/22315381/IJETT-V70I6P206

How to Cite?

R. Karunia krishnapriya, G. Vinodhini, R. Suban, K. Sakthivel, "Machine Learning based Encryption Framework for Privacy Preservation in Covid 19 Data," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 47-53, 2022. Crossref,

With the ongoing pandemic of COVID-19, numerous bits of intelligence are required to analyse the type of infection and mutation that is undergoing around the globe. However, while processing these data, privacy is another major field that requires concentration since it involves user data privacy. Hence, preserving privacy while processing the images is required for optimal management and security consideration of big data analytics. In this paper, the study models a machine learning method that is integrated with encryption models for optimal data encryption and thereby maintaining the privacy preservation of data. The privacy preservation using the proposed data encryption model is tested under two problems involving digit recognition and medical image application in classifying the computed tomography images of lungs from various covid-19 patients. The simulation was conducted to test the efficacy of the privacy preservation method in providing data privacy while the data has been classified. The simulation results show that the proposed machine learning encryption model enables optimal privacy of user data than other existing methods.

Privacy Preservation, Bigdata Analytics, Covid-19, Machine Learning.

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