Quantum-Resilient and Privacy-Preserving AIoT: A Secure Edge Intelligence Framework

Abstract

The merging of Artificial Intelligence (AI) with the Internet of Things (IoT) has sparked a swift transformation in AIoT systems, allowing for real-time intelligence in smart cities, industries, and homes. Yet, these advancements bring about increasing worries regarding data privacy, device trust, and potential security threats-particularly with the emergence of quantum computing. This paper introduces a secure and privacy focused AIoT framework that integrates Federated Learning with Differential Privacy, Zero-Knowledge Proofs (ZKP) for device authentication, and Post-Quantum Cryptography(CRYSTALSKyber) to protect model updates on the blockchain. Unlike conventional methods that depend on cloud processing and expose sensitive data, this innovative system allows for on-device model training through TinyML, ensuring that data remains on the device. A practical implementation using ESP32-S3 devices in both a smart classroom and home environment showcases the framework's effectiveness. The results indicate a 12% boost in privacy, a 35% reduction in communication costs, and an 8.7% increase in model accuracy compared to traditional methods. This architecture tackles significant unresolved challenges in AIoT by securing data at the edge, preventing device spoofing, and preparing for future quantum threats-making it an excellent choice for privacy-sensitive, real-time AIoT applications.