7 research outputs found
A novel approach to quality-of-service provisioning in trusted relay Quantum Key Distribution networks
In recent years, noticeable progress has been made in the development of quantum equipment, reflected through the number of successful demonstrations of Quantum Key Distribution (QKD) technology. Although they showcase the great achievements of QKD, many practical difficulties still need to be resolved. Inspired by the significant similarity between mobile ad-hoc networks and QKD technology, we propose a novel quality of service (QoS) model including new metrics for determining the states of public and quantum channels as well as a comprehensive metric of the QKD link. We also propose a novel routing protocol to achieve high-level scalability and minimize consumption of cryptographic keys. Given the limited mobility of nodes in QKD networks, our routing protocol uses the geographical distance and calculated link states to determine the optimal route. It also benefits from a caching mechanism and detection of returning loops to provide effective forwarding while minimizing key consumption and achieving the desired utilization of network links. Simulation results are presented to demonstrate the validity and accuracy of the proposed solutions.Web of Science28118116
A Novel approach to quality-of-service provisioning in trusted relay quantum key distribution networks
In recent years, noticeable progress has been made in the development of quantum equipment, reflected through the number of successful demonstrations of Quantum Key Distribution (QKD) technology. Although they showcase the great achievements of QKD, many practical difficulties still need to be resolved. Inspired by the significant similarity between mobile ad-hoc networks and QKD technology, we propose a novel quality of service (QoS) model including new metrics for determining the states of public and quantum channels as well as a comprehensive metric of the QKD link. We also propose a novel routing protocol to achieve high-level scalability and minimize consumption of cryptographic keys. Given the limited mobility of nodes in QKD networks, our routing protocol uses the geographical distance and calculated link states to determine the optimal route. It also benefits from a caching mechanism and detection of returning loops to provide effective forwarding while minimizing key consumption and achieving the desired utilization of network links. Simulation results are presented to demonstrate the validity and accuracy of the proposed solutions
Topological optimization of hybrid quantum key distribution networks
With the growing complexity of quantum key distribution (QKD) network
structures, aforehand topology design is of great significance to support a
large-number of nodes over a large-spatial area. However, the exclusivity of
quantum channels, the limitation of key generation capabilities, the variety of
QKD protocols and the necessity of untrusted-relay selection, make the optimal
topology design a very complicated task. In this research, a hybrid QKD network
is studied for the first time from the perspective of topology, by analyzing
the topological differences of various QKD protocols. In addition, to make full
use of hybrid networking, an analytical model for optimal topology calculation
is proposed, to reach the goal of best secure communication service by
optimizing the deployment of various QKD devices and the selection of
untrusted-relays under a given cost limit. Plentiful simulation results show
that hybrid networking and untrusted-relay selection can bring great
performance advantages, and then the universality and effectiveness of the
proposed analytical model are verified.Comment: 12 pages, 4 figure
Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet
Federated edge learning (FEL) is a promising paradigm of distributed machine
learning that can preserve data privacy while training the global model
collaboratively. However, FEL is still facing model confidentiality issues due
to eavesdropping risks of exchanging cryptographic keys through traditional
encryption schemes. Therefore, in this paper, we propose a hierarchical
architecture for quantum-secured FEL systems with ideal security based on the
quantum key distribution (QKD) to facilitate public key and model encryption
against eavesdropping attacks. Specifically, we propose a stochastic resource
allocation model for efficient QKD to encrypt FEL keys and models. In FEL
systems, remote FEL workers are connected to cluster heads via quantum-secured
channels to train an aggregated global model collaboratively. However, due to
the unpredictable number of workers at each location, the demand for secret-key
rates to support secure model transmission to the server is unpredictable. The
proposed systems need to efficiently allocate limited QKD resources (i.e.,
wavelengths) such that the total cost is minimized in the presence of
stochastic demand by formulating the optimization problem for the proposed
architecture as a stochastic programming model. To this end, we propose a
federated reinforcement learning-based resource allocation scheme to solve the
proposed model without complete state information. The proposed scheme enables
QKD managers and controllers to train a global QKD resource allocation policy
while keeping their private experiences local. Numerical results demonstrate
that the proposed schemes can successfully achieve the cost-minimizing
objective under uncertain demand while improving the training efficiency by
about 50\% compared to state-of-the-art schemes