Privacy-Aware Machine Learning Techniques for Secure Internet of Things Systems

Abstract

The rapid expansion of the Internet of Things (IoT) has enabled numerous industries to benefit from enhanced connectivity and automation. Despite these advancements, IoT devices pose serious privacy and security challenges due to the vast amounts of sensitive data they generate and transmit. The distributed and dynamic nature of IoT environments necessitates more sophisticated security measures. This paper proposes a privacy-aware machine learning (ML) approach for securing IoT systems, aiming to detect and prevent malicious activities without compromising user privacy. By employing privacy-preserving ML techniques such as federated learning and differential privacy, the method supports efficient and adaptive threat detection while ensuring data confidentiality. Comparative results clearly indicate that the proposed approach significantly outperforms existing solutions in terms of both overall performance and privacy protection, thereby offering a more robust and secure framework for safeguarding IoT networks under diverse and sophisticated attack scenarios. This research focuses on developing advanced, privacy-preserving machine learning methods tailored specifically for enhancing IoT security. By strategically combining federated learning techniques with lightweight encryption methods, the framework effectively protects sensitive data at the edge-device level, preventing unauthorized access or misuse. Experimental evaluations demonstrate that our proposed model achieves approximately 12% higher accuracy compared to conventional differential privacy methods, all while maintaining rigorous confidentiality standards and minimal computational overhead. These outcomes suggest promising potential for broad real-world applicability in secure IoT deployments.