PRIVACY PRESERVING MACHINE LEARNING IN HEALTHCARE FOR PANDEMIC PREDICTION USING GENOMIC DATA

Abstract

The demand for analyzing healthcare data can be attributed to the curiosity for personalized prediction, treatment, and monitoring. The rapid growth of healthcare information by the demand of healthcare requires better strategies for healthcare data analysis. However, while healthcare data analytics has been proposed to combination of information, network expertise, mixed in models that are trained on this private information; the use of late deep systems, improving an architecture time and complexity. Healthcare data is information about a patient's healthcare status. It includes various types of data such as structured and non-structured, or private and national healthcare data. The global movement of having health data for the public is producing many initiatives. While healthcare data analytics have demonstrated some promising results, there are still challenges, particularly in models trained in private data. Privacy Preserving Deep Learning techniques in the healthcare domain addressed the critical challenge of protecting the privacy of the patient and ensuring the judicious usage of data for models in machine learning. In this research, we have discussed comparative study of the key techniques which involve Federated Learning, Differential Privacy, Homomorphic Encryption, Secure Multi-party Computation and Synthetic Data Generation. These techniques will provide robust solutions for data-confidentiality and secure model training. This also discusses the amalgamation of these advanced technologies with regulatory compliance, which helps in emphasizing the potential of balancing innovation with ethical responsibility to transform healthcare. In recent times, there has been a rapid spread of pandemics caused by rapidly mutating viruses, such as SARS-CoV-2 which has present significant challenges for healthcare systems worldwide. The global health crises like COVID-19 underscore the need for predictive models that support containment and resource management. Genomic data is very crucial in providing critical insights into viral evolution and the mechanics of dynamics. Genomic datasets contain information that requires such computational methods that protect privacy. We have used federated deep learning architecture using genomic data for the pandemic prediction. We have achieved both data privacy by identifying key genomic features and implementing federated learning and robust model performance. Our results help in demonstrating the effectiveness of the method proposed by offering a scalable solution for the monitoring of pandemics.