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To address the privacy and security related challenges in Internet of Things (IoT) environment, we proposed a biometric method called BioSec to provide authentication in IoT integrated with edge consumer electronics with fingerprint authentication. Further, we ensured the security of biometric data both in the transmission channel and database with the standard encryption method. In this way, BioSec ensures secure and private communication among edge devices in IoT and Industry 4.0. Finally, we have compared three encryption methods used to protect biometric templates in terms of processing times and identified that AES-128-bit key encryption method outperforms others.

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes security and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, Machine Learning (ML), and Function as a Service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket (TLS) protocol-based security to secure the patient's health data. iFaaSBus outperforms response time compared to non-serverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and Area Under the Curve (AUC) values and the K-Nearest Neighbour model gives the highest accuracy rate of 97.51%.