PEL: Privacy Embedded Learning in Smart Healthcare Systems

The widespread use of healthcare data for online medical diagnosis has been made possible by deep learning advancements. However, entrusting computation and storage to unreliable external medical servers introduces security and privacy concerns. As a result, developing trustworthy deep learning algorithms has attracted growing interest in defending against privacy concerns in patient data. Federated learning was developed to protect sensitive data privacy by allowing computation on the client side. Privacy leakage in the communication channel of healthcare systems, through inference, free-riding, Man-in-the-Middle, model poisoning, and gradient attacks, is still a crucial issue. To address this, we introduce an efficient Privacy Embedded Learning (PEL) method that trains machine learning models without compromising privacy. This PEL method addresses how machine learning models handle privacy issues by securing privacy at the patient end, at a medical server and in communication media. To balance privacy protection and model performance, PEL uses edge intelligence-enabled federated learning to defend Smart Healthcare Systems from privacy attacks by applying artificial noise functions and an iteration-based Conventional Neural Network (CNN) model. PEL also offers gradient encryption in federated learning to protect the derived model parameters as gradients on communication media to protect users' privacy without revealing user-sensitive information. We also integrated Federated Edge Aggregator (FEA) into the proposed PEL method to offer a lower overhead than peer mechanisms. We compare the proposed method with existing work and evaluate performance with well-known datasets: COVID-19 chest X-rays and MNIST. The performance is demonstrated by testing accuracy of about 92% and good privacy protection when safeguarding patient and healthcare provider data.