FEELAP: Fuzzy Extractor-Based Efficient Lightweight Authentication Protocol for Edge-IoT Ecosystem in e-Healthcare

In modern edge IoT-assisted e-healthcare, biosensors are implanted within the human body to collect real-time physiological data. These vast amounts of sensitive data are then transmitted to a physician's device for diagnosis and real-time analysis via an edge server. The data transmission over an open network channel makes it susceptible to numerous vulnerabilities, including side-channel and impersonation attacks. Existing research has highlighted inefficiencies that deter extensive cryptographic operations required to ensure data security. It faces difficulties with side-channel attacks, impersonation attacks, or struggles with efficiency due to demanding lightweight cryptographic requirements. This article presents an efficient and lightweight authentication scheme using biometric fuzzy extractors, incorporating an edge server to enhance security within the edge IoT e-healthcare paradigm. The scheme's security and correctness have been rigorously and comprehensively analyzed using the well-regarded random oracle model (ROM) and ProVerif, along with a comprehensive assessment of known vulnerabilities. Experimental results and comparative analysis based on communication, computation costs, energy consumption, latency, runtime, and scalability demonstrate that the proposed protocol outperforms existing solutions.