Security and Privacy Frameworks for Access Control Big Data Systems

Abstract

In the security and privacy fields, Access Control (AC) systems are viewed as the fundamental aspects of networking security mechanisms. Enforcing AC becomes even more challenging when researchers and data analysts have to analyze complex and distributed Big Data (BD) processing cluster frameworks, which are adopted to manage yottabyte of unstructured sensitive data. For instance, Big Data systems' privacy and security restrictions are most likely to failure due to the malformed AC policy configurations. Furthermore, BD systems were initially developed toped to take care of some of the DB issues to address BD challenges and many of these dealt with the "three Vs" (Velocity, Volume, and Variety) attributes, without planning security consideration, which are considered to be patch work. Some of the BD "three Vs" characteristics, such as distributed computing, fragment, redundant data and node-to node communication, each with its own security challenges, complicate even more the applicability of AC in BD. This paper gives an overview of the latest security and privacy challenges in BD AC systems. Furthermore, it analyzes and compares some of the latest AC research frameworks to reduce privacy and security issues in distributed BD systems, which very few enforce AC in a cost-effective and in a timely manner. Moreover, this work discusses some of the future research methodologies and improvements for BD AC systems. This study is valuable asset for Artificial Intelligence (AI) researchers, DB developers and DB analysts who need the latest AC security and privacy research perspective before using and/or improving a current BD AC framework.

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