UCAP: A Novel Attribute Matching Authentication Model Using Knowledge Graph Approach

The exponential growth of big data and cloud computing has significantly expanded data collection and storage capabilities. However, traditional encryption methods often hinder efficient querying and matching operations, thereby limiting the potential of data applications. To address this challenge, this paper introduces the Universal Certified Attribute Pool (UCAP) model, a novel access control mechanism designed to ensure data privacy while enabling efficient attribute matching. The UCAP model operates within a knowledge graph-based attribute matching space, integrating attribute characteristics with information entropy metrics to establish a multi-tiered authorization framework. By leveraging graph neural network computing techniques, the model employs a scoring mechanism that enhances the accuracy and reliability of attribute matching. The UCAP model shows broad applicability in areas such as searchable encryption in cloud storage, medical record sharing among healthcare institutions, and secure data exchange between Internet of Things (IoT) devices. This paper specifically demonstrates the model's effectiveness in cross-border digital identity attribute authentication, highlighting its superior scalability and response speed in fuzzy matching scenarios compared to traditional homomorphic and attribute-based encryption methods.