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

Hadoop has become one of the key player in offeringdata analytics and data processing support for any organizationthat handles different shades of data management. Consideringthe current security offerings of Hadoop, companies areconcerned of building a single large cluster and onboardingmultiple projects on to the same common Hadoop cluster.Security vulnerability and privacy invasion due to maliciousattackers or inner users are the main argument points in anyHadoop implementation. In particular, various types of securityvulnerability occur due to the mode of data placement in HadoopCluster. When sensitive information is accessed by anunauthorized user or misused by an authorized person, they cancompromise privacy. In this paper, we intend to address theapproach of data placement across distributed DataNodes in asecure way by considering the sensitivity and security of theunderlying data. Our data placement strategy aims to adaptivelydistribute the data across the cluster using advanced machinelearning techniques to realize a more secured data/infrastructure.The data placement strategy discussed in this paper is highlyextensible and scalable to suit different sort of sensitivity/securityrequirements

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

Hadoop has become one of the key player in offeringdata analytics and data processing support for any organizationthat handles different shades of data management. Consideringthe current security offerings of Hadoop, companies areconcerned of building a single large cluster and onboardingmultiple projects on to the same common Hadoop cluster.Security vulnerability and privacy invasion due to maliciousattackers or inner users are the main argument points in anyHadoop implementation. In particular, various types of securityvulnerability occur due to the mode of data placement in HadoopCluster. When sensitive information is accessed by anunauthorized user or misused by an authorized person, they cancompromise privacy. In this paper, we intend to address theapproach of data placement across distributed DataNodes in asecure way by considering the sensitivity and security of theunderlying data. Our data placement strategy aims to adaptivelydistribute the data across the cluster using advanced machinelearning techniques to realize a more secured data/infrastructure.The data placement strategy discussed in this paper is highlyextensible and scalable to suit different sort of sensitivity/securityrequirements