Florian Wohner

Stephan Krenn, Omid Mir, Thomas Lorünser et al.

Secure long-distance communication in quantum key distribution (QKD) networks depends on trusted repeater nodes along the entire transmission path. Consequently, these nodes will be subject to strict auditing and certification in future large-scale QKD deployments. However, trust must also extend to the network operator, who is responsible for fulfilling contractual obligations -- such as ensuring certified devices are used and transmission paths remain disjoint where required. In this work, we present a path validation protocol specifically designed for QKD networks. It enables the receiver to verify compliance with agreed-upon policies. At the same time, the protocol preserves the operator's confidentiality by ensuring that no sensitive information about the network topology is revealed to users. We provide a formal model and a provably secure generic construction of the protocol, along with a concrete instantiation. For long-distance communication involving 100 nodes, the protocol has a computational cost of 1-2.5s depending on the machine, and a communication overhead of less than 70kB - demonstrating the efficiency of our approach.

Sebastian Gruber, Tobias Harzfeld, Christoph G. Schuetz et al.

In distributed optimization, multiple parties collaborate to find an optimal solution to a problem. Privacy-preserving distributed optimization uses techniques, such as secure multi-party computation (MPC), to protect the private inputs of each party. In time-critical settings, the runtime overhead introduced by privacy-preserving computations may prevent the optimization from finishing within the deadline. This paper presents an approach for privacy-preserving distributed optimization in time-critical settings that combines evolutionary algorithms for solution search and MPC for the evaluation of solutions. The approach reduces the impact of privacy-preserving computations on runtime and allows to return solution within the deadline. Obfuscation of evaluation results provides additional protection for private inputs from an honest-but-curious platform provider, but introduces a potential trade-off between protection and solution quality. This trade-off is investigated in experiments using a genetic algorithm for both the single-objective assignment problem and the traveling salesperson problem, as well as NSGA-II for the multi-objective assignment problem.