Privacy-Preserving Data in IoT-based Cloud Systems: A Comprehensive Survey with AI Integration

• Published in: arXiv.org
• Main Authors: Dhinakaran, D, Udhaya Sankar, S M, Selvaraj, D, Raja, S Edwin
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 01.01.2024
• Subjects: Access control; Artificial intelligence; Cloud computing; Internet of Things; Literature reviews; Machine learning; Privacy; Trends

As the integration of Internet of Things devices with cloud computing proliferates, the paramount importance of privacy preservation comes to the forefront. This survey paper meticulously explores the landscape of privacy issues in the dynamic intersection of IoT and cloud systems. The comprehensive literature review synthesizes existing research, illuminating key challenges and discerning emerging trends in privacy preserving techniques. The categorization of diverse approaches unveils a nuanced understanding of encryption techniques, anonymization strategies, access control mechanisms, and the burgeoning integration of artificial intelligence. Notable trends include the infusion of machine learning for dynamic anonymization, homomorphic encryption for secure computation, and AI-driven access control systems. The culmination of this survey contributes a holistic view, laying the groundwork for understanding the multifaceted strategies employed in securing sensitive data within IoT-based cloud environments. The insights garnered from this survey provide a valuable resource for researchers, practitioners, and policymakers navigating the complex terrain of privacy preservation in the evolving landscape of IoT and cloud computing