IBMESR: Towards Next-Generation Big Data Security with An Integrated Blockchain Model for Efficient, Scalable, and Robust Operations

Abstract As the utilization of big data environments becomes increasingly widespread, the demand for robust and efficient security mechanisms within this context becomes paramount, especially in real-time scenarios. Ensuring data integrity, trust, and availability in multi-cloud environments is of utmost importance. Existing solutions often fall short in these aspects, resulting in issues such as latency, energy inefficiency, and vulnerability to access attacks, among other drawbacks. Traditional security models designed for big data environments tend to rely on ineffective proof mechanisms and static encryption methods. These conventional approaches lead to increased data processing delays, heightened energy consumption, and reduced communication throughput. Furthermore, these existing models have demonstrated limited capabilities in accurately identifying unauthorized access attempts, thereby escalating security risks. This paper introduces an innovative security framework tailored for big data environments, which leverages a suite of cutting-edge technologies to address the limitations of current models. The proposed architecture integrates several key components, including a Proof of Spatial & Temporal Trust-based Blockchain for secure storage operations, Grey Wolf Optimization (GWO) for efficient blockchain sharding, Fully Homomorphic Encryption (FHE) for safeguarding data across various data nodes, Physical Unclonable Functions (PuFs) for resilient inter-data node communications, and Fuzzy Rule-Based Access Control to enhance overall security. Our research results demonstrate that this proposed model significantly mitigates the inefficiencies and vulnerabilities that currently plague big data security systems. Specifically, it achieves an 8.3% reduction in data processing delays, a 4.5% reduction in energy consumption, a 2.9% improvement in communication throughput, an 8.5% enhancement in the accuracy of detecting access attacks, and a 3.5% increase in access speed levels. This innovative security framework tailored for big data environments offers a comprehensive and effective approach to overcome the limitations of existing solutions. It represents a significant advancement in the field of big data security, contributing significantly to ongoing efforts aimed at creating more secure, efficient, and resilient big data ecosystems.