A Framework for Secure Big Data Analytics in Multi-Tenant Cloud Infrastructures

Abstract

This paper presents a comprehensive investigation into a framework for secure big data analytics in multi-tenant cloud infrastructures, focusing on the challenges of protecting sensitive information while ensuring efficient computational performance. The proposed framework addresses key security vulnerabilities originating from shared hardware resources, complex data handling processes, and the ever-growing volume of cloud-based data. By integrating robust cryptographic techniques with advanced scheduling algorithms, the framework seeks to guarantee confidentiality, integrity, and availability of tenant data in large-scale distributed environments. A mathematical basis for multi-tenant security is developed, incorporating sophisticated encryption schemes, secure key management methods, and computational offloading strategies. In addition, specific mechanisms for dynamic resource allocation are introduced to handle the fluctuating workload demands typical of big data applications. The paper examines theoretical models of potential adversarial behavior and quantifies associated risks through probabilistic estimations that capture both known and zero-day attacks. Furthermore, an experimental evaluation of the proposed framework is presented, demonstrating how optimized cryptographic protocols can significantly reduce overhead while retaining high standards of data security. The results highlight improved throughput, reduced latency, and efficient handling of large datasets under rigorous security constraints. Limitations of the framework and areas requiring further exploration, such as scalability bottlenecks under extreme workloads, are also discussed.