Implementation of Attribute-Base Control in Personal Data Protection

Abstract

This research addresses the growing concern of cybersecurity and access control in Python applications, providing actionable recommendations for improving Attribute-Based Access Control (ABAC) systems to better protect personal data. The study aims to evaluate ABAC's efficacy in managing access control within Python applications, particularly focusing on its ability to provide precise and fine-grained control over personal data access. By analyzing three key attributes—user roles, data classification, and access times—within Python applications, the research methodically assesses ABAC's performance and challenges in implementation. The findings, with a significant proportion of 70%, underscore ABAC's advantages over traditional models like Discretionary Access Control (DAC) and Role-Based Access Control (RBAC), emphasizing its capability to provide precise and fine-grained control over personal data access. Additionally, the research identifies and addresses three main challenges in ABAC implementation: attribute management complexity 15%, the necessity for standardization 10%, and interoperability issues 5%. This research has far-reaching implications, highlighting the importance of meticulous planning and modeling for successful ABAC deployment. By enriching our understanding of ABAC in Python-based environments, the study offers insights for enhancing cybersecurity measures and access control strategies in personal data protection.