Two current trends in the automotive industry are the increasing number of connected vehicles and automated driving. The former enables the use of different applications within the vehicle. These applications might be restricted to vehicles with certain features such as manufacturer or model.
To enable automated driving, the vehicle needs information about the road ahead. This information might be provided by an application. In order to keep the street information up to date connected vehicles share their sensor data. This data is then aggregated on a central server. Furthermore, it has a restricted spatial and temporal validity. Therefore, the vehicles also need to provide the corresponding time and position information.
When reporting position data, it is possible, for example, to generate movement profiles or to identify sensitive locations. Hence, it is critical which information different applications reveal about the corresponding vehicles.
Therefore, in this thesis we propose four different schemes which restrict the information applications can obtain from vehicles. The first scheme addresses the problem how a vehicle can authenticate itself privacy preserving based on attributes at an application without revealing all its attributes. The second scheme provides a solution for the question how two vehicles can authenticate each other for an application and exchange confidential data without disclosing their identity. The third scheme obfuscates the identity of a vehicle while sharing sensor data with a central server. The fourth scheme is related to the question how data can be distributed by a central server to all vehicles equipped with a particular application and located within a certain area without tracking the vehicles and knowing their subscribed applications. In addition, we outline how these schemes can be combined.
We demonstrate that each scheme is practical by presenting prototype implementations. Additionally, we simulate the second and third scheme in order to assess the impact on the vehicles privacy
