Data privacy protection for Hadoop
Posted on 15 June 2012.
Dataguise released DgHadoop, a data privacy protection and risk assessment solution for Hadoop. It provides compliance assessment and enforcement for centralized data privacy protection to simplify data compliance management, improve operational efficiencies and reduce regulatory compliance costs.


"We live in a time of data proliferation and much of today's 'new' data doesn't reside in traditional enterprise data sources such as relational databases or enterprise applications," said Manmeet Singh, CEO, Dataguise.

"The data can be anywhere, on premises or in the cloud, and companies need to reliably enforce data privacy policies regardless of where it is stored. DgHadoop meets this challenge by delivering enterprise-class sensitive data detection, protection and actionable intelligence for Hadoop, applying the most appropriate measures to ensure data integrity and regulatory compliance," Singh added.

Because environments such as Hadoop include very large data sets of mixed classifications and security sensitivities, they are particularly prone to data privacy breaches. And, because so much data is aggregated into one environment, the risk of data theft or accidental disclosure is substantially increased because it serves as a single point of access to potentially millions of records.

The detection and protection of sensitive data contained in these large data repositories is critical to the management of privacy policy enforcement and control to minimize the risk of data compromise.

DgHadoop was designed to overcome this challenge. It is a comprehensive, flexible and modular solution that delivers enterprise-class protection to sensitive data aggregated in Hadoop deployments. Offering actionable intelligence to decision makers about their compliance risk and mitigation policies, DgHadoop adapts easily to different deployments and data utilization needs.

"Big Data poses a big challenge to security technologies; both in the diversity of the data sets they store, and the sheer scale of the environments which leverage sensitive data to perform analytics," said Adrian Lane, Security Analyst and CTO, Securosis. "I see data masking as a key technology to addressing data security and privacy in these environments. Masking, unlike encryption solutions, can secure a myriad of data types and still preserve value in large analytics systems and Hadoop databases."





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