Multi-factor authentication (MFA) has become an essential method for enhancing security in authentication procedures by leveraging multi-dimensional authentication anchors, such as Biometrics-Based Authentication and One-time Password (OTP). However, MFA usually triggers for each login attempt and significantly impacts user usability. To this end, Risk-Based Authentication (RBA) is developed to achieve a better balance between user usability and security by dynamically checking the user authentication information. Opposite to the previous RBA designs that leverage static rules, this paper introduces Dynamic User Behavior Authentication (DUBA), an enhanced RBA design proposed to further improve both security and user experience. Our design uses probabilistic statistical methods to evaluate and score user behaviors. In this, authentication procedures can be dynamically adjusted in response to real-time user patterns and potential threats. Besides, DUBA introduces the weight adjust scheme that can efficiently defend against malicious behavior while improving usability, utilizing multi-dimensional behavioral data, such as login frequency, device information, and geographic location. We implement DUBA and evaluate its effectiveness by integrating it into the actual Single Sign-On (SSO) system in use on our campus. The results show that DUBA significantly reduces false positives and strengthens defenses against identity impersonation attacks.

Entanglement distribution between distant quantum nodes plays an important role in quantum networks. However, due to the unique properties of quantum mechanics and hardware limitations, entanglement resources in quantum networks are scarce. Quantum links that fail to meet request demands become bottleneck links, significantly hindering remote entanglement distribution in multi-request scenarios. In this paper, we propose an entanglement routing scheme called SwappingBoost that can effectively reduce resource consumption along entanglement distribution paths, alleviating the negative impact of bottleneck links. SwappingBoost first employs a decreasing resource reservation method to compensate for resource losses caused by failed entanglement swapping, freeing up pre-reserved resources on downstream links to accommodate other paths and requests. Besides, SwappingBoost introduces a path-priority-based rounding algorithm that achieves integer-level resource allocation while ensuring balanced resource allocation. Extensive simulation results demonstrate that SwappingBoost can effectively reduce the load of bottleneck links, enhancing network throughput while maintaining fairness among multiple requests.

In recent years, multi-hop Quantum Key Distribution (QKD) network has been proven as a promising solution through rigorous practices to provide end-to-end key exchange service for arbitrary communication parties. However, existing decentralized solutions still face critical challenges including consistency and fairness that stem from storable nature of quantum key material. Thus, in this paper, we first devise a Key Management and Service (KM&S) framework for decentralized multi-hop QKD networks, which provides functional decoupling and pipeline processing to guarantee flexibility and compatibility for practical implementation. After that, to address consistency and fairness challenges during end-to-end key exchange service, we focus on two aspects including local key management and end-to-end congestion control, and respectively propose an elastic key supply rate control scheme named AUTO and a Capacity Probing-driven Backpressure Flow Control (CP-BFC) scheme. Furthermore, we construct an experiment platform equipped with realistic QKD devices based on China metropolitan QKD network topology to implement the proposed framework and schemes, and conduct extensive experiments compared to representative schemes in existing studies. The experimental results show that AUTO&CP-BFC significantly outperforms representative schemes in terms of consistency and fairness.

Quantum communication networks enable advanced quantum applications through remote entanglement distribution among source-destination pairs. Despite efforts to optimize entanglement distribution, fairness in multi-request scenarios has been neglected, potentially causing issues like “request starvation”. To address such issue, this paper concentrates on the unique properties of entangled systems and introduces a process-oriented fairness metric, i.e., expected throughput, departing from conventional approaches used in classical networks. Furthermore, we propose an entanglement distribution scheme named Fair-EAS, which prioritizes entanglement allocation and selection for batching multiple requests to maximize overall throughput while maintaining max-min fairness. To facilitate a convenient solution, we transform the nonlinearity of the problem into an equivalent linear programming formulation and decouple the solution into offline and online phases. In the offline phase, we design a multi-round water-filling-like optimization algorithm to determine the optimal path set for predicting entanglement allocation. In the online phase, we introduce an adaptive compensation algorithm and an entanglement “fragment” exhaustion algorithm to dynamically adjust the path set based on successfully generated entangled pairs. Comprehensive simulations show that Fair-EAS outperforms the existing schemes in terms of fairness by significantly enhancing the minimum throughput and throughput deviation among multiple requests while maintaining an overall throughput close to the optimal level.

Quantum Key Distribution (QKD) technology leverages its inherent security advantages to ensure information-theoretic security for data transmission in networks. However, existing QKD networks still face critical challenges, including network congestion that stems from limited key resources and uneven resource allocation methods. Thus, in this paper, we focus on the issue of network congestion caused by bottleneck links and aim to achieve load balancing. Considering the limited key resources, we first introduce the key resource utilization ratio as an indicator of bottleneck links and formulate the resource allocation problem as an Integer Linear Programming (ILP) problem. To deal with the complexity of the ILP problem, especially in large-scale network scenarios, we design a heuristic algorithm that can obtain near-optimal solutions within polynomial time. Finally, we implement the proposed key resource allocation scheme in various real-world network topologies using a full-stack quantum network simulator. Compared to the existing algorithms, extensive results show that our method can reduce key resource consumption by up to 50% on bottleneck links and improve the robustness of QKD networks when facing burst quantum key agreement requests.

The entanglement transport protocol with a connection-oriented mode ensures the reliable distribution of remote entanglement by reserving dedicated resources on the selected path for users in a quantum network. In most existing protocols, entanglement generation and resource allocation operate with the support of global network-synchronized time slot. However, such synchronization in a large-scale quantum network is challenging, and the idealized time slot model is not conducive to continuous and concurrent requests. Meanwhile, different link performance in memory capacity and entanglement generation rate brings out critical issues, such as long distribution delay and low resource utilization, which has not been adequately addressed by the existing protocols relying on a heuristic adoption of TCP-like transport modes. In light of these observations, we propose a dynamic rate matching-based entanglement transport protocol called DRM-ETP, which allocates different memory units on each link along an entanglement distribution path. Moreover, DRM-ETP incorporates periodic forward and backward interactions to implement fine-grained feedback and a dynamic memory allocation based on priority differentiation. These mechanisms mitigate congestion and unfairness arising from resource contention among burst requests on shared links. Extensive simulation results demonstrate that DRM-ETP significantly outperforms the existing protocols in terms of throughput and resource utilization, with less distribution delay and higher fidelity. Moreover, DRM-ETP exhibits rapid and fair convergence when handling burst requests. Our study opens up possibilities for deploying efficient entanglement transport in quantum networks, thereby holding the promise of enhanced compatibility and novel functionality.

Quantum key distribution (QKD) networks can provide information-theoretically secure key distribution between distant end nodes through key relaying. In QKD networks, the key relay protocol is vital since it provides the coordination specifications between nodes for key relaying and thus directly determines the performance, especially as the network scale expands. However, most existing protocols adopt a synchronous contend-and-relay approach, where the contention and consumption of quantum keys occur simultaneously, neglecting the storable nature of quantum keys and presenting significant challenges in reliability and quantum key utilization. To tackle these challenges, in this paper, we propose an asynchronous key relay protocol (AKRP). AKRP considers the storable nature of quantum keys, and adopts a reserve-then-relay approach to achieve lossless and zero-queuing key relaying through precise management of quantum keys and requests. On this basis, to further improve the performance of the proposed AKRP, we design two enhanced mechanisms, i.e., collision detection and resolution mechanism and multipath routing extension. The former enhances the consensus efficiency of AKRP and provides fine-grained key utilization on each link, and the latter utilizes quantum keys on possible relay paths and thus effectively copes with quantum key exhaustion. By conducting extensive experiments on a semi-physical real QKD network platform, results demonstrate that AKRP is superior to existing schemes in terms of end-to-end key throughput, quantum key consumption, and relaying latency.

Quantum networks, which work by establishing entanglement between distant quantum end nodes (known as end-to-end entanglement distribution), are the promising infrastructure for quantum applications. However, the inherent loss in quantum channels and quantum decoherence contribute to the scarcity of entanglement resources in quantum networks. Consequently, there is an inevitable gap between available entanglement resources and requests’ demands, significantly hindering concurrent end-to-end entanglement distributions. In this paper, we present NarrowGap, an end-to-end entanglement distribution design that can alleviate the negative impact of entanglement resource scarcity on the request service capability of quantum networks. At the heart of NarrowGap, the resource transfer scheme (RTS) is designed to transfer idle entanglement resources to boost the bottlenecks’ capacities based on the unique feature of entanglement swapping, thus narrowing the gap between available entanglement resources and requests’ demands for end-to-end entanglements. Besides, NarrowGap presents a resource allocation scheme (RAS) to guarantee fairness, considering both the success probability of end-to-end entanglement distribution and each request’s demand, to address resource competition in bottlenecks. Extensive simulations demonstrate that NarrowGap outperforms three representative schemes and can achieve more than twice the performance improvement in request service rate.

Nowadays, Quantum Key Distribution (QKD) has garnered widespread attention due to its ability to provide symmetric secret keys with information-theoretic security in point-to-point communication. Additionally, key relay technology has been introduced to complete end-to-end key distribution between remote parties by consuming keys in the intermediate links. The routing problem of selecting paths for key relay technology becomes crucial as it directly impacts the network's performance. In this paper, we focus on addressing the routing problem for a specific scenario to serve applications with real-time requirements. Real-time is a critical necessity for supporting various applications, such as video chatting and calling. To satisfy real-time requirements and achieve Quality of Service (QoS) provision to guarantee the completion time, we introduce a distributed routing protocol called Distributed Routing Protocol Based on Key Reservation (Q-RoKR). It reserves keys in advance along the selected path, thereby satisfying real-time requirements. We also propose a priority-awareness mechanism to address resource competition and make efficient use of keys in links. Extensive experiments demonstrate that our protocol effectively meets the real-time requirements and significantly improves throughput. Furthermore, our key efficiency approaches the optimal bound when compared to other comparison schemes.

Mobile devices are becoming increasingly capable of delivering intelligent services by leveraging deep learning architectures such as deep neural networks (DNNs). However, due to the compute-intensive nature of these tasks, mobile devices often struggle to handle them independently, leading to the exploration of collaborative inference as a promising solution for achieving low-latency mobile intelligence. Despite its potential benefits, many challenges need to be addressed in realizing the full potential of inference acceleration. This article presents a collaborative device-edge inference optimization framework as a promising solution to inference acceleration. The framework comprises fundamental modules, including the parameters generator (PG), accuracy predictor (AP), delay calculator (DC), and optimizer (OP), which are specifically designed to identify the optimal set of parameters for model compression, DNN partition, and feature compression. To illustrate its implementation, an example of a deep CNN network is introduced, and the collaborative inference latency optimization is formulated as a mixed-integer programming problem. The implementation of a specific algorithm instance using a quantum-inspired OP within the optimization framework is then presented. A multiple regression-based inference accuracy prediction model is proposed to maintain inference accuracy close to that of the original network while significantly reducing the time consumption during the offline phase. Through various simulation scenarios involving inference tasks of AlexNet and RegNet on CIFAR-10, incorporating diverse hardware computation specifications and wireless communication link conditions, the proposed framework demonstrates superior performance in terms of inference acceleration compared to the compared methods.

Remote entanglement distribution in an efficient and reliable manner, especially in the context of a large-scale quantum network with multiple requests, remains an unsolved challenge. The key difficulties lie in achieving spontaneous and precise control over the entanglement distribution procedure, as multiple nodes need to reach a consensus on how to perform it. From the network aspect, allocating link-layer entangled pairs as resources to achieve high efficiency is also challenging. To address these issues, we propose a decentralized Reliable Entanglement Distribution Protocol (REDP) for large-scale networks. The protocol operates in a Forward-Backward Propagation (FBP) manner, where consensus is reached hop-by-hop and disseminated to all nodes on the path. We further use probabilistic analysis and quasi-static modeling to seek the fairness and efficiency of the network based on the above transmission model. Accordingly, we introduce a Source Window Strategy (SWS) and an Entanglement Allocation Strategy (EAS) to assign sending windows and allocate resources for multiple requests, ensuring a high level of fairness and efficiency from a network perspective. Through systematic simulations involving both classical and quantum communication protocols, we demonstrate that REDP outperforms existing approaches in terms of fairness, throughput, and fidelity performance.

Entanglement distribution between distant quantum nodes plays an essential role in realizing quantum networks’ capabilities. In addition to path selection, remote entanglement distribution involves two pivotal quantum operations, i.e., entanglement generation and entanglement swapping. The existing studies mainly adopt two methods, i.e., Tell-and-Generation (TAG) and Tell-and-Swapping (TAS), to manage these two quantum operations on a selected path. However, both methods fatally introduce redundant stop-and-wait processes, which are detrimental to the performance of remote entanglement distribution in terms of latency and fidelity. To achieve low-latency and high-fidelity entanglement distribution between far-off quantum nodes, we propose a segment-based method consisting of an entanglement generation algorithm and a segment design to diminish the unnecessary stop-and-wait processes. The entanglement generation algorithm adopts a concurrent design to establish entanglement links using the one-demand generation model, thus effectively reducing waiting time compared to hop-by-hop and parallel designs. The segment design is proposed to split a long-distance path into multiple short-haul segments with the similar ability to swap entanglement, and these segments build multi-hop entanglement connections in parallel. Extensive simulations show that the segment-based method significantly outperforms the existing methods, including TAG and TAS, in entanglement distribution latency and effectively mitigates fidelity attenuation.

The quantum network is designed to connect numerous quantum nodes and support various ground-breaking quantum applications. Most of these applications require communicating parties to share entangled pairs. Therefore, entanglement routing, a technology distributing entangled pairs between distant quantum nodes, plays a vital role in realizing quantum networks’ capability. However, due to the limitation of quantum memory size and quantum decoherence, the entangled pairs shared by adjacent quantum nodes can hardly satisfy concurrent entanglement routing requests, thus leading to severe network congestion. In this paper, we propose a novel congestion mitigation (CM) scheme to tackle such bottleneck problems. The basic idea of CM is to “recycle” idle link-level entanglement resources from well-resourced links to bottleneck links utilizing a unique enabling technology of quantum networks, called entanglement swapping. CM can increase the capacity of each bottleneck link, thus overcoming resource limitations to improve resource utilization and network throughput. To complete our work, we also propose a swapping-based entanglement routing design, including path selection and resource allocation algorithms. Extensive simulations show that our design can significantly alleviate network congestion and improve the request service rate of quantum networks compared to the traditional entanglement routing designs.