Blockchain-Enabled Privacy and Homomorphic Encryption with Voting Classifier in IIoT

In this virtual world, there are billions of physical devices are sharing their data via the Industrial Internet of Things (IIoT). These data transactions centralize with security and authentication for passing the data among IIoT applications. However, this centralization is exposed to security limitations due to its rapid growth and it is difficult to scale up the applications which acts as a critical challenge for the researchers. To improve the reliability of the IIoT application, federated learning with blockchain technology is utilized for data security. The UCI medical repository is taken as the input dataset which is fetched to k-means clustering for a cluster then homomorphic encryption for encrypting the input data. This encrypted data is verified by the signature sign-in process for authentication purposes. The blockchain accepts these data via decryption using a private key. Finally, the ensemble model (SVM, RF, Decision tree) classifies the result into five categories such as stage 1, stage 2, stage 3, stage 4, and stage 5 of heart disease. The experimental result shows the performance of the proposed system is better than the existing algorithms. Thus, we integrate blockchain and federated learning to provide an enhanced environment for IIoT applications.