  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
