Juri Dispan

In the future, vehicles will be able to exchange information with each other and with infrastructure. In this way, applications for Cooperative, Connected and Automated Mobility (CCAM) such as Cooperative Intersection Management (CIM) can be realised. Since these applications are safety-critical, it is important for the application to evaluate the trustworthiness of the data provided by other entities before using them. How trustworthy the data are can be specified by the sender of the message. The structure of the messages is specified by ETSI (European Telecommunications Standards Institute). However, the current standard does not yet include trust opinions. Therefore, in this thesis/project, you will come up with a format for ETSI messages (e.g. CAMs) in which trust opinions for the provided data can be included. In the second step, you will implement your message format in a traffic and network simulation tool. Finally, you will compare the performance of the extended ETSI messages to standard ETSI message format based on certain performance metrics.

Subjective Logic (SL) is a mathematical framework for reasoning under uncertainty. It is useful for expressing opinions on how reliable information is (so-called Trust Opinions) and performing computations on these opinions. At our institute, we research applications of SL in the automotive domain, e.g. to express trust in data received from a sensor or from other vehicles. Current implementations of SL internally use floating-point arithmetic (IEEE 754) for performing calculations. However, IEEE 754 floating-point numbers are prone to introducing rounding errors. In safety-critical domains, failing to account for such errors might lead to catastrophic consequences. In this thesis/project, you will investigate the potential impact of floating-point errors in SL calculations and develop strategies to minimise it. You can choose your approach freely: whether you work theoretically (e.g. through a detailed study of literature) or practically (e.g. through implementing a test environment and explaining the observed effects) is up to you.

This project's goal is to implement and benchmark several secure shortest path algorithms. In the scenario we consider, a service provider is in possession of a graph (e.g. of shipping routes). A client would like to know the distance between two vertices A and B in this graph (which could, for example, be the duration to go from A to B). However, the service provider does not want to reveal its graph to the client, and neither does the client want to reveal A and B to the service provider. Previous work by different authors has proposed several algorithms that can achieve this. However, the evaluation of these algorithms has not been consistent, because different software frameworks and experimental setups were used. For this reason, this project's task is to implement several algorithms for secure calculation of the shortest distance using a suitable framework for Secure Multiparty Computation (e.g. MP-SPDZ). In a second step, these implementations should be used to benchmark the performance of the considered algorithms.

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.

We introduce a combination of Shamir's secret sharing and dining-cryptographers networks, which provides (n-|attackers|))-anonymity for up to k-1 attackers and has manageable performance impact on dissemination. A k-anonymous broadcast can be implemented using a small group of dining cryptographers to first share the message, followed by a flooding phase started by group members. Members have little incentive to forward the message in a timely manner, as forwarding incurs costs, or they may even profit from keeping the message. In worst case, this leaves the true originator as the only sender, rendering the dining-cryptographers phase useless and compromising their privacy. We present a novel approach using a modified dining-cryptographers protocol to distributed shares of an (n, k)-Shamir's secret sharing scheme. All group members broadcast their received share through the network, allowing any recipient of k shares to reconstruct the message, enforcing anonymity. If less than k group members broadcast their shares, the message cannot be decoded thus preventing privacy breaches for the originator. We demonstrate the privacy and performance results in a security analysis and performance evaluation based on a proof-of-concept prototype. Throughput rates between 10 and 100 kB/s are enough for many real applications with high privacy requirements, e.g., financial blockchain system.

A k-anonymous broadcast can be implemented using a small group of dining cryptographers to first share the message, followed by a flooding phase started by group members. Members have little incentive to forward the message in a timely manner, as forwarding incurs costs, or they may even profit from keeping the message. In worst case, this leaves the true originator as the only sender, rendering the dining-cryptographers phase useless and compromising their privacy. We present a novel approach using a modified dining-cryptographers protocol to distributed shares of an (n,k)-Shamir's secret sharing scheme. Finally, all group members broadcast their received share through the network, allowing any recipient of k shares to reconstruct the message, enforcing anonymity. If less than k group members broadcast their shares, the message cannot be decoded thus preventing privacy breaches for the originator. Our system provides (n-|attackers|)-anonymity for up to k-1 attackers and has little performance impact on dissemination. We show these results in a security analysis and performance evaluation based on a proof-of-concept prototype. Throughput rates between 10 and 100 kB/s are enough for many real applications with high privacy requirements, e.g., financial blockchain system.