  This paper discusses one of the most significant challenges of
next-generation big data (BD) federation platforms, namely, Hadoop access
control. Privacy and security on a federation scale remain significant concerns
among practitioners. Hadoop's current primitive access control presents
security concerns and limitations, such as the complexity of deployment and the
consumption of resources. However, this major concern has not been a subject of
intensive study in the literature. This paper critically reviews and
investigates these security limitations and provides a framework called BD
federation access broker to address 8 main security limitations. This paper
proposes the federated access control reference model (FACRM) to formalize the
design of secure BD solutions within the Apache Hadoop stack. Furthermore, this
paper discusses the implementation of the access broker and its usefulness for
security breach detection and digital forensics investigations. The efficiency
of the proposed access broker has not sustainably affected the performance
overhead. The experimental results show only 1\% of each 100 MB read/write
operation in a WebHDFS. Overall, the findings of the paper pave the way for a
wide range of revolutionary and state-of-the-art enhancements and future trends
within Hadoop stack security and privacy.
