AI Meets Blockchain: A Secure Privacy Preserved Framework for IoT Devices Using Blockchain
Muhammad Kashif, Sohail Sarwar, Muhammad Safyan · 2025
The recent integration of blockchain technology with the Internet of Things (IoT) has created a new platform for secure and decentralized communication among IoT devices. However, this integration has raised concerns about privacy preservation due to the inherent transparency and immutability of the blockchain. To address these challenges, our paper presents a comprehensive framework for privacy preservation in IoT-based blockchain systems. We propose a privacy-preserving blockchain-based framework that classifies transactions as public or private at the IoT level, using a combination of low-computation cryptographic techniques and a low-complexity consensus protocol to ensure data confidentiality and privacy. Additionally, we integrate an anomaly-based intrusion detection system (IDS) to monitor and address threats in real time, making IoT networks resilient against evolving cyber threats. We evaluated our proposed system model through comprehensive simulations and experiments, demonstrating the practicality and effectiveness of our approach in real-world IoT scenarios. Our findings show that our proposed protocol can comprehensively safeguard the privacy of IoT data while maintaining the security and transparency inherent in blockchain-based systems. This private-by-design model provides a real-world solution to privacy concerns in IoT-based blockchain systems, contributing to the development of secure, decentralized IoT communication.