Advancing Cloud Security Optimization for Scalable Integration of Big Data Analytics and Io T Workloads in Modern Cloud Computing Infrastructures

The exponential growth of Internet of Things (Io T) devices and Big Data analytics workloads in cloud environments has introduced unprecedented security challenges that traditional protection mechanisms cannot adequately address. This paper proposes a novel integrated security framework combining homomorphic encryption, secure multi-party computation, and zero-trust architecture specifically designed for cloud-based Io T and Big Data environments. Our methodology employs a layered security approach that implements protection at the edge, during transmission, and within cloud processing layers, ensuring data confidentiality and integrity throughout the analytics pipeline. Through comprehensive simulation of diverse Io T workloads including smart city, healthcare, and industrial Io T scenarios, we demonstrate that our framework maintains robust security while adding minimal computational overhead?showing just 18.7% performance degradation compared to 64.3% with conventional encryption in processing-intensive analytics tasks. The proposed model effectively balances the competing demands of security and performance scalability, addressing critical gaps in existing cloud security paradigms for Io T and Big Data integration. Our findings provide a foundation for developing adaptive security frameworks capable of meeting the evolving challenges of large-scale, data-intensive cloud environments.