There is an increasing demand for processing large volumes of unstructured data for a wide variety of applications. However, protection measures for these big data sets are still in their infancy, which could lead to significant security and privacy issues. Attribute-based access control (ABAC) provides a dynamic and flexible solution that is effective for mediating access. We analyzed and implemented a prototype application of ABAC to large dataset processing in Amazon Web Services, using open-source versions of Apache Hadoop, Ranger, and Atlas. The Hadoop ecosystem is one of the most popular frameworks for large dataset processing and storage and is adopted by major cloud service providers. We conducted a rigorous analysis of cybersecurity in implementing ABAC policies in Hadoop, including developing a synthetic dataset of information at multiple sensitivity levels that realistically represents healthcare and connected social media data. We then developed Apache Spark programs that extract, connect, and transform data in a manner representative of a realistic use case. Our result is a framework for securing big data. Applying this framework ensures that serious cybersecurity concerns are addressed. We provide details of our analysis and experimentation code in a GitHub repository for further research by the community.
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.
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- PAR ID:
- 10072092
- 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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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3180457.3180463
