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 analyze 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. Finally, a use case from the area of banking is presented to illustrate the concrete application of the transformations.

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