Blockchain-Enabled Machine Learning Models for Secure IoT Data Analytics

Abstract

The integration of Internet of Things (IoT) devices has led to an unprecedented surge in data generation, necessitating robust analytics frameworks. However, the decentralized nature of IoT networks poses significant security and privacy challenges. This paper proposes a novel approach combining blockchain technology with machine learning models to enhance the security and efficiency of IoT data analytics. We present a comprehensive framework that leverages blockchain's immutability and distributed consensus mechanisms to ensure data integrity, while employing advanced machine learning algorithms for predictive analytics. Our experimental results demonstrate improved data security, reduced latency, and enhanced prediction accuracy compared to traditional centralized approaches.