Proceedings of the 26th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) 2023, to appear,
Multi-model consistency management is used to keep models with overlapping information consistent during model-based systems engineering. Checking consistency between different models requires model data to be exchanged in some form between the modeling tools. However, in industrial practice such models can contain confidential data that must not be revealed to unauthorized parties, e.g., other departments or external companies. Therefore, it is a critical problem when the consistency management tool does not respect this. We found that none of the current multi-model consistency management approaches in literature consider this problem and provide suitable protections to prevent possible confidential data theft caused by consistency checking. Hence, we propose an approach and implementation for confidentiality preservation in multi-model inconsistency detection that does not reveal confidential model data to unauthorized parties and increases the trustworthiness of the consistency checking process regarding data confidentiality. In this paper we explain the problem and propose our basic solution idea and evaluation plan for our doctoral research.