When sensitive microdata regarding people is published, choosing a secure anonymization method is vital. Comparing the effectiveness of different anonymization methods is challenging due to their structural differences. Many re-identification attacks exist that attempt to reverse these methods and identify individuals. However, prior work typically evaluates privacy risks in isolation - focusing on a single anonymization technique at a time. The proposal in this work is to compare different anonymization methods by simulating re-identification attacks on them. In a first step, an ontology that models the landscape of attacks and anonymization methods is created. Additionally, all attacks available in the literature are retrieved and analyzed with regard to which anonymization methods are susceptible to them. As specified in the ontology, the anonymization methods, attacks and their relationships are organized into a structured knowledge graph. Finally, a framework is created, making our contributions seamlessly accessible. The framework allows access to the knowledge graph via an interactive visualization. Additionally, attacks that can be simulated on custom data anonymized by different methods are implemented. After simulating the attacks, their success can serve as a state-of-the-art approximation of the actual re-identification risk. The attack’s success aids in bridging the comparability gap between structurally different anonymization methods.