Using Obfuscating Transformations for Supporting the Sharing and Analysis of Conceptual Models
Several initiatives have been started that promote the collaborative creation, sharing and analysis of conceptual models. In order to maintain confidentiality and protect intellectual property, sensitive data has to be removed from the models or at least sufficiently abstracted. We derive and analyse four types of obfuscating transformations for conceptual models that have been inspired by existing methods from the area of source code obfuscation and privacy preserving data mining. The transformations are visually illustrated and evaluated by their complexity, resilience and scope of analysis.