HadoopSec: Sensitivity-aware Secure Data Placement Strategy for Big Data/Hadoop Platform using Prescriptive Analytics

Abstract

Hadoop has become one of the key player in offering
data analytics and data processing support for any organization
that handles different shades of data management. Considering
the current security offerings of Hadoop, companies are
concerned of building a single large cluster and onboarding
multiple projects on to the same common Hadoop cluster.
Security vulnerability and privacy invasion due to malicious
attackers or inner users are the main argument points in any
Hadoop implementation. In particular, various types of security
vulnerability occur due to the mode of data placement in Hadoop
Cluster. When sensitive information is accessed by an
unauthorized user or misused by an authorized person, they can
compromise privacy. In this paper, we intend to address the
approach of data placement across distributed DataNodes in a
secure way by considering the sensitivity and security of the
underlying data. Our data placement strategy aims to adaptively
distribute the data across the cluster using advanced machine
learning techniques to realize a more secured data/infrastructure.
The data placement strategy discussed in this paper is highly
extensible and scalable to suit different sort of sensitivity/security
requirements.