Integrating an Encryption Standard and Cloud Computing to Improve the Safety of Sensitive Medical Data

The Internet of Medical Things (IoMT) has improved people's quality of life by making it possible to connect biomedical sensing equipment for health monitoring. Current e-healthcare also recognizes the value of technologies like health data exporting to the cloud. Both of these approaches have their limitations, though. The biggest challenge is presented by the limited resources of sensor devices and the need to ensure the privacy of sensitive medical information. Sensitive data refers to the subset of personal information that necessitates an extra layer of security because it can reveal extremely private aspects of an individual's life. A person's health information is extremely personal. Encryption is the safest method for transferring and storing sensitive information. Only approved individuals with the correct key have access, so the possibility of abuse within the firm is greatly diminished. Numerous business functions are moving to cloud computing, from data backup and recovery to email and virtual desktops to software development. To effectively use solutions, pressing issues must be addressed swiftly. Taking the necessary precautions is essential for avoiding harm when receiving medical care. This research intends to provide patient-centric protection for sensitive medical data in the cloud by employing a tiered architecture of security activities and the notion of Integrated Encryption Standard and Mobile Cloud Computing (IES-MCC). IES-MCC utilized Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance the security settings and eliminate the smoothness to clean and secure cloud data storage paths. The IES-MCC model is used to examine the simulation assessment scores of the various elements and features, and the results are the most precise and efficient compared to an existing method.