Preserving Privacy in Industrial IoT: A Machine Learning Framework Enhanced by Poly1305 Encryption

Abstract

Abstract: The convergence of Industrial Internet of Things (IoT) systems and machine learning has ushered in new paradigms for predictive maintenance, anomaly detection, and quality control in industrial sectors. Though, the sensitive nature of data generated by IoT devices raises significant privacy and security concerns. To address these challenges, this review paper presents a thorough examination of safeguarding privacy machine learning frameworks in the context of Industrial IoT environments, leveraging the robust Poly1305 encryption algorithm. This paper commences by surveying the landscape of Industrial IoT applications, highlighting the urgency of safeguarding sensitive data without compromising the potential insights gleaned from machine learning models. It then delves into the intricacies of Poly1305 encryption, elucidating its strengths in ensuring data integrity, authenticity, and confidentiality. The integration of Poly1305 encryption within machine learning pipelines is explored, considering encryption at data collection, transmission, storage, and analysis stages. The review systematically analyses a spectrum of data learning models deployed in Industrial IoT scenarios, spanning predictive maintenance to quality enhancement. Each model's compatibility with Poly1305 encryption is assessed, along with performance implications and security guarantees. The paper navigates through key obstacles including key management, computational overhead, and interoperability concerns. In summation, this review paper not only serves as a comprehensive guide for researchers and practitioners in the realm of Industrial IoT and machine learning but also contributes to the ongoing discourse on safeguarding data privacy while harnessing the transformative power of data-driven insights