Role-Based Access Control Technique with Trino for restriction in Hive-based Data Warehouse

In the fast-changing field of big data analytics, it is crucial to have strong security and access control measures. This paper presents a new Role-Based Access Control (RBAC) technique that is specifically designed for Hive-based data warehouses, using Trino as a query engine. Our method is intended to effectively manage and enforce detailed access policies, addressing the ever-changing requirements of data security in large-scale data environments. The text describes the challenges of data warehouse security and the limitations of current access control methods in Hive data warehouses. It then presents a proposed RBAC model that integrates with Trino to provide a secure, flexible, and scalable solution. The model includes user authentication, role assignment, permission grants, and access controls. It allows for customized roles to fit different user profiles and data access requirements, ensuring the protection of sensitive data while maintaining flexibility for diverse analytical needs. The efficacy of the model is demonstrated through a series of tests, evaluating its performance in terms of security, scalability, and impact on query execution times.