KG-PEM: A Data Privacy Protection Assessment Framework Based on Knowledge Graph

To enhance the effectiveness of a data management platform in assessing and mitigating risks to information and data privacy, we present a data privacy evaluation model (KGPEM) that leverages knowledge graph technology, combining traditional methods, to create a scalable, open, and interconnected framework. The model's architecture uses a dualpronged approach, incorporating both top-down and bottomup strategies with two primary layers, including a model layer, which encompasses evaluation indices, logical frameworks, and historical case studies, and a data layer. The evaluation process is further strengthened by integrating Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) methods, resulting in the creation of an evaluation matrix that assigns weights to key metrics. This enables a quantitative analysis of privacy evaluations, providing a theoretical foundation and practical guidance for real-world applications. The feasibility of this approach is validated through the application of an evaluation logic graph to actual case studies, demonstrating its potential for effective privacy assessment and management.