Blockchain-IoT Security Architecture for Disability-Inclusive E-Learning: A Privacy-Preserving Framework
Year: 2026
Type: article
Abstract: The educational system is experiencing an historic opportunity to provide people with disabilities the same access to digital learning as non-disabled individuals; however, this digital learning revolution has created new complexities related to both privacy and security issues when it comes to assistive technologies that are part of Internet of Things (IoT). This research article systematically analyzes seventy articles (2012 - 2025), and outlines a proposed architectural framework consisting of four layers. These layers include IoT assistive technology, privacy preserving edge processing, blockchain based validation, and adaptive interface design. Additionally, a Proof of Concept was conducted using 45 participants which demonstrated that the proposed architecture could achieve authentication successes greater than 96% across all disability categories (visual = 99.1%; motor = 96.4%; hearing = 97.8%; cognitive = 92.3%), in less than 200 ms. This represents a 23.4% increase in speed over legacy password based architectures (baseline= 78%). We also address the oracle problem by implementing cryptographically signed nodes on each device within our IoT network. In addition, we have resolved the long standing issue of the accessibility-security paradox by architecturally decoupling these two competing requirements. Statistical testing demonstrated a statistically significant increase in performance (p< .001), with a 95% Confidence Interval of [94.2%, 98.1%], and still maintains compliance with Web Content Accessibility Guidelines 2.1 Level AAA standards. Therefore, this framework will serve as evidence-based guidance for developers building secure and accessible E-Learning Platforms that align with the paradigm of IoT, Blockchain, and E-Health.
Institution: Université Hassan 1er
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7166316264