Abstract
Data security and privacy issues are magnified by the volume, the variety, and the velocity of Big Data and by the lack, up to now, of a reference data model and related data manipulation languages. In this paper, we focus on one of the key data security services, that is, access control, by highlighting the differences with traditional data management systems and describing a set of requirements that any access control solution for Big Data platforms may fulfill. We then describe the state of the art and discuss open research issues.
Highlights
The term Big Data refers to a phenomenon characterized by “5 V”
The support provided by Big Data platforms for the storage and analysis of huge and heterogeneous datasets cannot find a counterpart within traditional data management systems
We focus on access control, by first identifying a set of requirements that any access control solution for Big Data platforms should address
Summary
The term Big Data refers to a phenomenon characterized by “5 V”. By analysing huge Volumes of data with a high Variety of formats, Big Data analytic platforms allow making predictions with high Velocity, in a timely manner, low Veracity, with low uncertainties, and with a high Value, namely, with an expected significant gain (Jin et al 2015). In Ulusoy et al (2015), a framework denoted GuardMR has been proposed, to enforce fine grained Role-based Access Control (RBAC) (Ferraiolo et al 2001) within Hadoop3, a very popular Big Data analytics platform built on top of MapReduce.
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