A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities

Abstract

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.

IABHE: Identity and Attribute-based Honey Encryption Algorithm and Deep Learning-based Key Generation for Securing Big Data in Hadoop Framework

“…Tall, A.M. and Zou, C.C., [1] devised an Achieve Attribute-Based Access Control (ABAC) for Attribute-Based Access Control in Big Data. This approach was highly flexible and performed significant configurations in assigning attributes to users, but it was not successful in detecting security and misconfiguration issues by including more logging capabilities and status monitoring to address security findings.…”

“…Generally, big data is represented by 3 V's of information, namely high variety, velocity, and volume [13]. Over the past decades, big data analysis has become a key research topic because of the increasing demand for processing huge quantities of unstructured data from various applications [1]. Moreover, large quantities of big data originated from enterprises due to the rapid growth of Information Technology (IT).…”

“…A substantial amount of data is transferred via digital applications that require extra dynamic framework execution, processing assets, and storage space [9]. The security risk of a loss of privacy is the major characteristic of big data, which is due to the compilation of data from various sources [1]. Ideally, the increasing use of web sensors, downloaded apps, social networking sites, smartphones, and so on generates large quantities of sensitive data that are highly vulnerable to attacks.…”

“…Hadoop is employed in the cloud computing framework to provide solutions to all these issues [9]. Nowadays, various cloud service providers, like Microsoft, Google, and Amazon utilize Hadoop-based services to provide secure service to users [1].…”

Beaf:BD – A Blockchain Enabled Authentication Framework for Big Data

“…Honor et al ( 2021) proposed a scheme that addresses the problem of data trustworthiness and provenance in IoT systems by providing an immutable and tamper-proof record of data transactions.Marco et al ( 2023) used a set of metrics and quality attributes to assess the level of trustworthiness of BD. Tall et al (2023) proposed a framework that allows for fine-grained control over data access based on a set of attributes associated with the data. Jeong et al (2015) proposed a model aiming to enhance the security of HDFS by improving the authentication process and preventing unauthorized access to the data.…”