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Title: An Attribute-Based Access Control Model for Secure Big Data Processing in Hadoop Ecosystem
Apache Hadoop is a predominant software framework for distributed compute and storage with capability to handle huge amounts of data, usually referred to as Big Data. This data collected from different enterprises and government agencies often includes private and sensitive information, which needs to be secured from unauthorized access. This paper proposes extensions to the current authorization capabilities offered by Hadoop core and other ecosystem projects, specifically Apache Ranger and Apache Sentry. We present a fine-grained attribute-based access control model, referred as HeABAC, catering to the security and privacy needs of multi-tenant Hadoop ecosystem. The paper reviews the current multi-layered access control model used primarily in Hadoop core (2.x), Apache Ranger (version 0.6) and Sentry (version 1.7.0), as well as a previously proposed RBAC extension (OT-RBAC). It then presents a formal attribute-based access control model for Hadoop ecosystem, including the novel concept of cross Hadoop services trust. It further highlights different trust scenarios, presents an implementation approach for HeABAC using Apache Ranger and, discusses the administration requirements of HeABAC operational model. Some comprehensive, real-world use cases are also discussed to reflect the application and enforcement of the proposed HeABAC model in Hadoop ecosystem.  more » « less
Award ID(s):
1736209 1538418 1423481 1111925
PAR ID:
10072092
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ABAC’18: 3rd ACM Workshop on Attribute-Based Access Control, March 19–21, 2018, Tempe, AZ,
Page Range / eLocation ID:
13 to 24
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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