Risk monitoring strategy for confidentiality of healthcare information

Concrete privacy endeavours to give Confidentiality, Integrity, and Availability (CIA) measures to secure traffic streams in sensitive healthcare applications are a necessity. When talking about the access of sensitive healthcare data or the confidentiality of highly esteemed data, the first thing that comes to mind is that it should be secured. Sensitive healthcare data needs to be protected to restrict illegal access, exposure, and/or manipulation. As there is no such protection by which we can make our systems fully secure, the most acceptable methodology is to perform layered modelling of safety measures. This paper intends to provide a mathematical description of the Modular Encryption Standard (MES) and the augmentation of condition-centric risk monitoring of confidential information to provide a layered model for securing healthcare data confidentiality. Decision-making regarding the risk monitoring strategy of MES is augmented using a machine learning approach based on a Fuzzy Inference System amalgamated with Neural Networks. Result analysis shows that MES has less than a 0.005 error-rate and a 97% precision-rate, which elucidates its desideratum towards increasing security risks.