Securing Data Communication in the Cloud Using Machine Learning-Based Blockchain Approach

Internet of Things (IoT) devices are used to communicate with each other. Cloud Computing (CC) is utilized to store and analyze the data in IoT for solving security issues. Cloud security is vital for numerous users who are concerned about data security in cloud. Recently, many blockchain methods are developed in the CC environment but, the data confidentiality and integrity were not improved with less time. To address these problems, a new machine learning-based blockchain technology called the Universal Estimator Regressive Single-Block-Length Compressed Hash Blockchain (UERSBLCHB) Method is introduced. IoT devices are employed together patient data. The proposed UERSBLCHB Method performed data regression analysis and secured data communication. Patient data is examined with Universal Estimator Regression via bivariate correlation. Safe data broadcasts were performed with Matyas–Meyer–Oseas Cryptographic Hash-based Blockchain method. Hash values of every patient data are created by Matyas–Meyer–Oseas compression. Hashed results stored into blockchain and perform secure data communication with higher data confidentiality and integrity and less processing time. Simulation of proposed and existing methods are performed in Java with MHEALTH dataset The outcome of UERSBLCHB Method increased confidentiality of 15%, integrity of 17%, accuracy by 20%, reduced processing time by 49%, and space complexity by 38%, than the traditional approaches.