Enhancing security and privacy protection for MapReduce processing: the initial simulation work flow

Abstract

MapReduce programming model allows the processing of massive amount of data in parallel through clustering across a distributed system. The tasks for MapReduce have been categorized into areas which are data management and storage, data analytics, on line processing and security and privacy protection. For sensitive data uploaded by users, it must be protected from any unauthorized access to ensure the integrity, authenticity and privacy of the data. It is important that, data at rest, data in transit and nodes is managed securely by ad-dressing the elements of data security and privacy protection which are auditing, access control and privacy. The purpose of this study is to enhance the prominence of security and privacy protection for MapReduce model. An existing study is more specifically tailored to structure based requirements. Whilst, there is a need to continue finding solutions for MapReduce in better handling big data security and privacy protection concerning the unstructured data. This paper presents the initial workflow of the simulation set up of MapReduce processing using the Hadoop platform to demonstrate an enhancement for security and privacy protection access control by implementing Whitelist to control access in MapReduce processing.