Securing the Patient’s Breast Cancer Data using Blockchain-based IBE with Deep Learning Model in IoT

Abstract

Computational intelligence (CI) and artificial intelligence (AI) have a portion to offer in the way of making healthcare systems smart and long-lasting. These technological advances have the potential to lessen our environmental impact while simultaneously increasing expectations for performance. The use of electronic health monitoring systems in healthcare management has been crucial. E-health has the potential to offer patients useful and efficient tracking tools. However, the existing E-Health system has protection disputes. However, there are safety concerns with the present online healthcare system. Doctors with bad intentions might collude with CSPs to compromise their patients' electronic health records (EHRs) or quickly leak EHR information to other opponents for financial gain. Patients might be manipulated by clinicians who work in collusion with a Patient Healthcare Monitoring Service Provider (PHMSP). EHRs for financial gain or disclose the HER content of EHRs directly to other adversaries. Recently, block-chain has emerged as one of the most potent strategies for maintaining privacy and security. The current security issues in e-health monitoring systems are expected to be replaced by this promising security method. To prevent unauthorised parties from accessing encrypted data, blockchain uses encryption. Using a computational intelligence approach, this study presented a blockchain-based encryption framework based on identity-based encryption (IBE) to deliver secure solutions. Furthermore, the study effort utilises optimised deep learning (ODL) to forecast the patient's condition from breast cancer input data. The suggested method improves performance, with an accuracy of 0.93 during training and 0.91 during validation.