The General Data Protection Regulation (GDPR) has significantly increased the incentive and effort for companies to process personal data in compliance with the law. This includes the creation, distribution, storage and deletion of personal data. Non-compliance with the GDPR and other legislation now poses a significant financial risk to companies that work with personal data. Because of this reason, the present project studied on the technical evaluation of decentralization based on de-identification procedures for personal data in the automotive sector. For this, use cases were identified through a scientific literature review. The following use cases were identified and analyzed with regard to data, benefits, model and sensible data: Traffic flow prediction, Energy demand prediction, Eco-routing, Autonomous driving, Vehicular object detection, Parking space estimation. Furthermore, attack scenarios and general countermeasures against these attacks were discussed. To do so, relevant transmission paths, data types and trust scenarios were considered.