Enhancing Data Security and Privacy in Cloud-based Big Data Systems: A Focus on Encryption and Access Control

Abstract

Cloud-based big data systems offer vast storage and computational capabilities, yet they pose significant security and privacy risks. Encryption and access control mechanisms have emerged as key strategies for ensuring confidentiality, integrity, and availability within these environments. Implementing robust encryption approaches can protect data at rest, in transit, and during processing. Access control frameworks, on the other hand, enforce stringent rules governing data sharing and policy enforcement. The complexities introduced by large-scale data ingestion, geographically dispersed storage nodes, and dynamic real-time analytics necessitate advanced solutions that integrate seamlessly with the underlying cloud infrastructure. The blending of symmetric and asymmetric cryptographic algorithms, along with emergent techniques that allow computations on encrypted data, promises stronger protection without incurring excessive performance overhead. Fine-grained access control solutions, including dynamic role-based schemes and attribute-based systems, preserve data confidentiality while allowing data owners to share information in a flexible manner. This work explores mathematical approaches for modeling encryption schemes, formal definitions of security, and advanced key management techniques to fortify cloud-based platforms against a rapidly evolving threat landscape. It further discusses strategies for addressing scalability challenges, ensuring efficient computation, and aligning with stringent regulatory demands in diverse, multi-tenant environments. Emphasis is placed on proposing comprehensive methods that jointly integrate encryption and access control for sustainable, high-assurance cloud deployments.