Addressing Data Privacy Concerns in Digital Emerging Technologies
Hanan Ahmad Aldowah, Shafiq Ul Rehman, Sohail Ahmed Shahani · 2025
In this digital age, data is one of the most valuable assets for organizations. Recent advances in the Internet of Things (IoT), cloud computing, and data analytics have enabled efficient data collection. IoT, a crucial set of smart technologies, is anticipated to drive the next information technology revolution and the development of big data analytics. However, widespread data collection and the lack of security in IoT devices raise significant privacy concerns. This study reviews current literature and discusses concepts and approaches for data privacy in IoT from various perspectives. It identifies several challenges and provides comprehensive solutions to address data privacy issues. The proposed solutions encompass technical, academic, and industry viewpoints. Additionally, the integration of machine learning (ML) and deep learning (DL) technologies into IoT systems introduces further privacy and security challenges. ML and DL models often require access to large datasets containing sensitive information, necessitating advanced privacy-preserving techniques such as differential privacy and federated learning. These methods aim to protect individual data while enabling robust model training. Furthermore, the study explores the impact of adversarial attacks on ML and DL models within IoT environments and discusses strategies to enhance model robustness and security. Despite these efforts, major issues remain in designing and deploying IoT solutions. This study concludes with recommendations and future research directions to enhance IoT privacy and security, particularly in the context of emerging ML and DL technologies.