Chaotic Key-Based Secure Data Access Protocol for IoT Healthcare

Abstract

The Internet of Things (IoT) has emerged as a disruptive paradigm in modern biomedical engineering, paving the way for seamless remote patient monitoring and immediate, real-time clinical interventions. Despite these transformative capabilities, the open and public nature of wireless communication links exposes the e-healthcare ecosystem to severe security exploits, making the secure transmission and persistent storage of highly confidential medical telemetry a major technical challenge. To address these vulnerabilities, this thesis introduces a robust Chaotic Key-Based Secure Data Access Protocol tailored specifically for decentralized e-Healthcare networks. The proposed architectural framework safely bridges three main entities: Medical Sensing Devices (MSDs), Cloud Data Servers (CDS), and Remote Healthcare Doctors.

This protocol involves the continuous gathering and analysis of critical patient physiological indicators, including but not limited to electrocardiogram (ECG), electroencephalogram (EEG), peripheral oxygen saturation (SpO2), blood pressure, and core body temperature, all while adhering to strict cryptographic requirements. The sensitive healthcare data is encrypted with high entropy using a dynamic chaotic key based approach to ensure zero-trust compliance prior to storing in the cloud. Only authorized medical professionals can access and decrypt the patient electronic health records (EHR) using a multi-factor permission loop. A thorough security review has found that the system remains very strong against devastating threat vectors such as node impersonation, malicious replay, man-in-the-middle (MitM) and physical device capture attacks. Empirical analyses and experimental benchmarks also verify a clear reduction in transmission bandwidth and computing latency, and a significant increase in authentication accuracy. Ultimately, the proposed protocol establishes a secure, lightweight, highly scalable, and privacy-preserving framework for next-generation healthcare data management.

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