Enhancing Service Efficiency and Ensuring Privacy in Distributed Computing Environments through a MapReduce-Based Framework.

Abstract

This research focuses on enhancing service efficiency and privacy assurance in distributed computing environments, specifically within the Collaborative Big Data Computation and Multiple Public Clouds (CBDC-MPC) paradigm. The study explores authentication mechanisms within CBDC-MPC, emphasizing their implications for risks, security provisioning, and authentication. A crucial connection is established with the MapReduce paradigm to align the proposed authentication framework with service efficiency goals. The research includes a comparative analysis of entity authentication techniques within CBDC-MPC, aiming to optimize security levels while minimizing overhead costs. The overall narrative positions authentication within CBDC-MPC as an integral part of a larger framework, where service efficiency, privacy, and authentication seamlessly converge.