OPTIMIZING SERVICE EFFICIENCY AND SAFEGUARDING PRIVACY IN DISTRIBUTED COMPUTING ENVIRONMENTS VIA A MAPREDUCE-POWERED FRAMEWORK

Abstract

This research delves into the augmentation of service efficiency and privacy assurance in distributed computing environments, with a specific focus on the Collaborative Big Data Computation and Multiple Public Clouds (CBDC-MPC) paradigm. The investigation scrutinizes authentication mechanisms within CBDC-MPC, shedding light on their implications for risks, security provisioning, and authentication protocols. A pivotal nexus is forged with the MapReduce paradigm to align the proposed authentication framework with the overarching goals of service efficiency.The study encompasses an in-depth comparative analysis of various entity authentication techniques within CBDC-MPC, with the primary objective of optimizing security levels while concurrently minimizing associated overhead costs. The broader narrative positions authentication within CBDC-MPC as an indispensable component of a comprehensive framework, wherein service efficiency, privacy considerations, and robust authentication seamlessly converge. This multifaceted approach seeks to establish a cohesive and well-integrated foundation for advancing the capabilities and security of distributed computing environments within the CBDC-MPC framework.

Key words: Distributed computing, Collaborative Big Data Computation (CBDC), Multiple Public Clouds (MPC), Authentication mechanisms, Service efficiency, Privacy assurance, MapReduce paradigm.