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Securing Web Data and Privacy in AIoT Systems
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Author(s): Marius Iulian Mihailescu (Universitatea Spiru Haret Bucureşti, Romania)and Stefania Loredana Nita (Military Technical Academy “Ferdinand I”, Romania)
Copyright: 2024
Pages: 45
Source title:
Semantic Web Technologies and Applications in Artificial Intelligence of Things
Source Author(s)/Editor(s): Fernando Ortiz-Rodriguez (Tamaulipas Autonomous University, Mexico), Amed Leyva-Mederos (Universidad Central "Marta Abreu" de Las Villas, Cuba), Sanju Tiwari (Tamaulipas Autonomous University, Mexico), Ania R. Hernandez-Quintana (Universidad de La Habana, Cuba)and Jose L. Martinez-Rodriguez (Autonomous University of Tamaulipas, Mexico)
DOI: 10.4018/979-8-3693-1487-6.ch007
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Abstract
The exponential growth of Artificial Intelligence of Things (AIoT) has resulted in an unparalleled fusion of AI with IoT technologies, giving rise to intricate systems that present vast opportunities for automation, productivity, and data-centric decision-making. Nevertheless, this amalgamation also poses substantial obstacles regarding safeguarding online information and upholding confidentiality. The chapter extensively examines the difficulties associated with these issues and the tactics employed to surmount them. The chapter commences by delineating the distinctive susceptibilities inherent in AIoT systems, with a particular emphasis on how the interconnection of AI and IoT technologies gives rise to novel avenues for data breaches and privacy infringements. It then explores the most recent approaches and technologies used to protect data sent over AIoT networks. These include improved encryption methods, secure data transfer protocols, and solutions based on blockchain technology. A substantial chunk of the chapter focuses on privacy-preserving strategies in AIoT. The text examines the equilibrium between data usefulness and privacy protection. It delves into techniques like anonymization, differential privacy, and federated learning as means to safeguard user data while ensuring the effectiveness of AIoT systems. The chapter also examines regulatory and ethical factors, thoroughly examining current and developing legislation and regulations that oversee data security and privacy in AIoT. The content incorporates case studies and real-world examples to demonstrate the pragmatic implementation of theoretical principles. Ultimately, the chapter predicts forthcoming patterns and difficulties in this swiftly progressing domain, providing valuable perspectives on possible AIoT security and privacy protocol advancements. This resource is vital for professionals, researchers, and students engaged in AIoT, cybersecurity, and data privacy. It provides them with the necessary information and tools to protect against the ever-changing threats in this dynamic field.
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