Risk monitoring strategy for confidentiality of healthcare information. (May 2022)

• Record Type: Journal Article
• Authors: Rizwan, Muhammad; Shabbir, Aysha; Javed, Abdul Rehman; Srivastava, Gautam; Gadekallu, Thippa Reddy; Shabir, Maryam; Hassan, Muhammad Abul
• Is Part Of: Computers & electrical engineering. Volume 100(2022)
• Publication Date: 2022-05
• Subjects: Modular Encryption Standard (MES) -- Healthcare -- Cloud computing -- Machine learning -- Adaptive neuro-fuzzy inference system -- Data confidentiality -- Layered modelling -- Risk monitoring; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107833
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: 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. Graphical abstract: Highlights: Propose an approach for securing healthcare data confidentiality. Provide mathematical modelling for the entire working of Modular Encryption Standard. Enhance the decision-making strategy of Modular Encryption Standard using fuzzy logic.