Trust-based node selection improves quantum key networks coverage
Reliability-Guided Trusted Repeater Node Selection in QKD-Enabled Metro Optical Networks
Networking and Internet Architecture
Summary
Quantum Key Distribution (QKD) lets people share secret keys securely but only works over short distances without extra help. The authors found that not all nodes that help extend QKD networks can be fully trusted because of security risks like hacks or insiders. They created a way to score how trustworthy each node is and use that score to pick the best nodes for extending the network. Their method improved the network's coverage by almost 9% compared to traditional methods. This helps make long-distance secure communication more reliable.
What this means in practice
- •For optical network engineers: Use trust-based scoring to select reliable repeater nodes for extending secure quantum key distribution networks in metropolitan areas.
- •For network security teams: Improve resilience of key management by identifying potentially compromised nodes in quantum-secured optical networks using trust metrics.
Authors
Arup Kumar Marik, Basabdatta Palit, Sadananda Behera
Abstract
Quantum Key Distribution (QKD) in optical networks can provide information-theoretic security but is limited by its operability range, thereby requiring repeater nodes for extended coverage. Existing works typically assume that all repeater nodes and their associated key management systems are fully trusted, overlooking the risks posed by software exploits and insider threats. To address this, in this work, we propose a Bayesian fusion-based model to quantify the trustworthiness of each node. We have formulated a reliability-guided Trusted Repeater Node (TRN) selection framework for metro optical networks, where each node is assigned a trust score. We have subsequently mapped this trust score to link weights, which in turn has been used for reliable path computation for TRN selection, using the Dijkstra algorithm. We have also ranked the nodes using a composite score combining eigenvector and betweenness centrality, to capture their topographical relevance in the network. Simulation results on a reference topology demonstrate that the proposed method achieves approximately 8.93% higher path coverage than traditional centrality-based approaches like degree centrality, using the same number (around ten) of TRNs, thereby supporting reliable and resilient TRN selection for QKD-enabled optical networks.