Decentralized anomaly detection and data privacy preservation for IoT networks using blockchain and homomorphic encryption

Abstract

The rapid increase in IoT networks has led to tremendous growth while introducing considerable challenges related to security and privacy issues. Traditional IDS are not fully equipped to mitigate the complexities developed by the extensive volumes of data and resource constraints inherent in an IoT environment. The system proposed here presents a decentralized one that brings blockchain technology with homomorphic encryption and more sophisticated machine learning models like ConvLSTM and GRU to enhance anomaly detection and data privacy for IoT systems. Edge preprocessing, SHA-256 hashing, and digital signatures are applied as components in the framework used respectively for secure integrity and authenticity of verification and security before storing in the blockchain. It thus ensures that all changes to the data are traced and noticed. L-Diversity is implemented to better protect users’ privacy so that anonymous data cannot be re-identified. Finally, with blockchain integration, scalability is enhanced in the system due to its maintenance of a decentralized ledger for the transparent recording of actions. The results of this experiment show an incredible accuracy of 99.90%, which indicates the superiority of this proposed method over existing solutions in terms of anomaly detection, energy efficiency, privacy preservation, and system stability.