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Abstract The evolution of quantum computers is considered a serious threat to public‐key cryptosystems (e.g. RSA, ECDSA, ECDH, etc.). This is indeed a big concern for security of the Internet and other data communication and storage systems. The reason is that public‐key schemes are the basis in the generation of shared symmetric keys that are used to perform data encryption/decryption in communication and data transfer protocols. One possible approach to address this issue is to use Quantum Key Distribution (QKD) (instead of public‐key schemes) for the ultra‐secure generation of symmetric keys. QKD is a physical layer technology that allows two parties (equipped with optical communication interfaces) to generate secure random keys over a quantum channel that is immune to eavesdropping threats. The keys are then used by symmetric encryption schemes (e.g. AES) to encrypt data over classical channels. This allows us to have data encryption/decryption without needing a public‐key scheme. However, due to its inherent characteristics, the implementation of QKD has mostly been considered in particular contexts only (e.g. backhaul networks, point‐to‐point connections, optical networks, etc.). This indeed limits the utility of QKD technology to only some particular applications while it has the potential to be used in a wide range of used cases. Motivated by this (increasing the usability of QKD technology), in this study, the authors propose a model that enables SDN‐based networks to utilise QKD technology and provide QKD security service (i.e., random key generation service) to network applications and security protocols in a practical and efficient way. In the proposed approach, secret keys are generated based on the distribution of quantum entanglement between QKD nodes deployed in the network. The significant characteristic of our proposed model is that it does not rely on quantum repeaters to operate. This also improves the efficiency of the employed QKD mechanisms in terms of the key generation rate.

Accurate load forecasting is critical for electricity production, transmission, and maintenance. Deep learning (DL) model has replaced other classical models as the most popular prediction models. However, the deep prediction model requires users to provide a large amount of private electricity consumption data, which has potential privacy risks. Edge nodes can federally train a global model through aggregation using federated learning (FL). As a novel distributed machine learning (ML) technique, it only exchanges model parameters without sharing raw data. However, existing forecasting methods based on FL still face challenges from data heterogeneity and privacy disclosure. Accordingly, we propose a user-level load forecasting system based on personalized federated learning (PFL) to address these issues. The obtained personalized model outperforms the global model on local data. Further, we introduce a novel differential privacy (DP) algorithm in the proposed system to provide an additional privacy guarantee. Based on the principle of generative adversarial network (GAN), the algorithm achieves the balance between privacy and prediction accuracy throughout the game. We perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

The blockchain-enabled smart logistics market is expected to grow worth USD 1620 billion and at a compound annual growth rate of 62.4%. Smart logistics ensures intelligence infrastructure, logistics automation, real-time analysis of supply chain data synchronization of the logistics process, cost transparency, unbroken shipment tracking all the way down to the transportation route, etc. In the smart logistics domain, significant advancement and growth of the Internet of Things (IoT) sensors are evident. However, the connectivity of IoT systems, including Tactile Internet, without proper safeguards creates vulnerabilities that can still be deliberately or inadvertently cause disruption. In view of this, we primarily notice two key issues. Firstly, the logistics domain can be compromised by a variety of natural or man-made activities, which eventually affect the overall network security. Secondly, there are thousands of entities in the supply chain network that use extensive machine-learning algorithms in many scenarios, and they require high-power computational resources. From these two challenges, we note that the first concern can be addressed by adding blockchain to IoT logistic networks. The second issue can be addressed using 6G. This will support 1-μs latency communications, support seamless computing at the edges of networks, and autonomously predict the best optimal location for edge computing. Motivated by this, we have highlighted motivational examples to show the necessity to integrate 6G and blockchain in smart logistic networks. Then, we have proposed a 6G and blockchain-enabled smart logistic high-level framework. We have presented the key intrinsic issues of this framework mainly from the security and resource management context. In this paper, recent state-of-the-art advances in blockchain enabled next-generation smart logistic networks are analyzed. We have also examined why 6G and not 5G would be compatible with the smart network. We have introduced five different use cases of blockchain technology in smart logistics. Later, this paper discusses some important concerns that blockchain in smart logistics might face. We have also provided potential solutions to tackle these concerns.

Ubiquitous Internet of Things (IoT) devices have fueled plenty of innovations in the emerging network paradigms. Among them, IoT edge caching has emerged as a promising technique to cope with the explosive growth in network data traffic, with Quality of Service (QoS) improved and energy saved. However, the intrinsic storage limitations of the edge servers poses a critical challenge for the IoT edge caching system. Enabling edge servers to cooperate with each other can provide a potential perspective to improve the edge storage utilization widely discussed. Nevertheless, it also incurs an additional communication overhead, eventually making the caching system more complex. As a result, how to perform an efficient cooperative caching becomes a critical issue. Thus, in this paper, we propose a deep reinforcement learning-based cooperative edge caching approach, which allows the distributed edge servers to learn to cooperate with each other. Specifically, edge servers determine their cache actions based on the local caching state. After that, the centralized remote server evaluates these actions and feeds back the evaluation results to edge servers for subsequent caching actions optimization. We show that, by designing an appropriate reward function, our approach promotes cooperation between edge servers as well as improving the system hit rate. On this basis, we consider a practical and reasonable scenario with inconsistent data item size and propose a novel multi-agent actor-critic caching algorithm. Extensive simulation results demonstrate the performance improvement using our proposed solution over three other caching algorithms.

Currently, data centers have become an attractive candidate for users that require IT resources in the form of virtual networks to run their applications. Optimal mapping of the virtual network on the top of the substrate network with resource constraint is called virtual network embedding (VNE) problem. Most of the VNE algorithms are general algorithms for random topology and not suitable for data centers due to particular topological characteristics. To solve the VNE problem in data centers, this paper develops a topology-oriented algorithm based on the Discrete Particle Swarm Optimization (DPSO). We first develop a maximum spanning algorithm to compute the ranking of virtual nodes based on, not only its bandwidth and degree, but also its connectivity in the entire virtual network. Then, the virtual networks are embedded onto the substrate network according to the connectivity ranking result by a DPSO-based algorithm, in which we also propose a topological heuristic information of substrate network and combine it into the particle search process for boosting convergence speed and revenue/cost ratio of substrate network. The evaluation results show that the proposed algorithm can improve the optimization performance of VNE by comparing with a few existing algorithms.

Social networks have become part of today's life; however, they also create numerous privacy problems for their users as they reveal sensitive information. Thus, the privacy preservation has become a big problem in social networks and there are several researches related to this topic. However, the current research achievements for this issue are only ad hoc solutions and the broad picture of the problem is not clear. This paper surveys a wide range of related researches and realises that the topic includes many sub‐areas; for example, the privacy in publishing social network data for the use of third‐party consumers or the privacy of users in the leakage of individuals’ information to unexpected people in their social circle. The study also analyses the advantages as well as limitations of proposed solutions. On the basis of that the research outlines key outstanding issues and recommends directions for the future research. Moreover, the research also investigates common privacy metrics and popular privacy‐preserving techniques to provide readers some basic tools to resolve open problems in the topic. The ultimate purpose of this research is to pave a solid background for people, who are interested in the topic, to do research further.

Due to the limited service capabilities of centralized controllers, it is difficult to process high volume of flows within reasonable time. This particularly degrades the strict quality of service (QoS) requirements of interactive media applications, which is non-negligible factor. To alleviate this concern, distributed deployments of software-defined network (SDN) controllers are inevitable and have gained a predominant position. However, to maintain application specific QoS requirements, the number of resources used in network directly impacts the capital and operational expenditure. Hence, in distributed SDN architectures, issues such as flow arrival rate, resources required and operational cost have significant mutual dependencies on each other. Therefore, it is essential to research feasible methods to maintain QoS and minimize resources provisioning cost. Motivated from this, we propose a solution in a distributed SDN architectures that provides flow-balancing (with guaranteed QoS) in pro-active operations of SDN controllers, and attempts to optimize the use of instance resources provisioning costs. We validate our solution using the tools of queuing theory. Our studies indicate that with our solution, a network with minimum resources and affordable cost with guaranteed application QoS can be set-up.

The Online Social Networks (OSN) open a new vista serving millions of users and have reshaped the way people interacts. Unfortunately these networks are an emerging platform for cybercrimes such as sending malicious URLs, spams, etc. which causes a huge financial loss and social damage. In this paper, it is reviewed that OSN is a new cybercrime platform for threating agents because of the security pit falls in the existing centralised architecture and their driven functionalities. This article surveys the detailed analysis of the current state of the Online Social Networks in perspective of security andprivacy issues. Additionally in this paper we presents various types of attacks that can be mounted via OSN and the defence measures against each of the attack. This literature also highlights on the types of vulnerabilities and threats in OSN. Significant threat categories and risks associated as well as a scope to circumventing these threats and vulnerabilitiesare also introduced.