“Evaluating Subjective Trust Networks through Secure Multiparty Computation,” Project or Bachelor or Master's thesis, J. Dispan (Supervisor), F. Kargl (Examiner), Inst. of Distr. Sys., Ulm Univ., 2023 – Open
In the future, vehicles will exchange information regarding the current traffic situation and planned maneuvers. While this has the potential to improve safety and fuel efficiency though cooperative driving, it opens up a novel attack vector: malicious actors might inject incorrect information, which could lead to accidents and thus poses a serious threat to safety. One approach to mitigate such attacks makes use of Subjective Trust Networks: vehicles form Trust Opinions on other vehicles, which are expressed using Subjective Logic and stored in a graph structure. Different vehicles can merge their Trust Networks in order to gain a more complete picture of the trustworthiness of their communication partners and make more informed decisions. However, privacy and safety considerations forbid that different vehicles simply exchange their trust networks. This thesis/project should investigate the feasibility of merging an evaluating Subjective Trust Networks using Secure Multiparty Computation (SMPC). For this, it is first necessary to precisely define the task at hand: Which calculations must be performed under SMPC in order to protect confidential information? Which information cannot be protected? Second, a prototype for an example scenario in which vehicles merge and evaluate their trust Networks should be implemented using a suitable framework for SMPC. Third, benchmarks should be performed that show the (non-)applicability of SMPC for the described use-case.