Abstract Spatial skyline queries represent a specialized category of skyline queries, applicable in various domains such as facility location, crisis management, and travel or event planning. The emergence of secure spatial skyline queries carries substantial practical implications. In this paper, we address the challenge posed by the point-geometry dependency problem inherent in existing spatial skyline query algorithms. Our approach involves a transformative strategy that simplifies the query into a more tractable range query problem. Building on this transformation approach, we design an efficient and secure spatial skyline query method for encrypted data, which requires alternating between ciphertext and plaintext queries. To ensure both security and optimal performance, we execute plaintext queries within a trusted execution environment. Experiments demonstrate the efficiency and effectiveness of our approach.

Currently, traditional network passive defense mechanisms like firewalls prove inadequate against the escalating sophistication of cyber threats. There is an urgent need for more advanced proactive defense strategies, such as network security situation prediction (NSSP). NSSP aims to predict the future development of network through historical and current data. Nevertheless, the majority of existing prediction approaches are capable of achieving either short-term or long-term prediction individually, yet there remains a need for enhancing prediction speed to meet the requirements for practical deployment. Therefore, This paper proposes an integrated long and short-term prediction method called RSMHA. This approach draws on Split-Attention Networks (ResNeSt) and Multi-Head Attention (MHA) to maximize feature extraction capabilities and comprehensively enhance both long-term and short-term prediction abilities. The methods chosen in RSMHA are highly efficient, allowing for a streamlined model structure and enhanced running speed. We evaluated RSMHA using both a real-world intrusion detection system (IDS) alert dataset and the UNSW-NB15 dataset. The experimental results demonstrate that, in comparison to other state-of-the-art methods, our RSMHA approach reduces the prediction error to a certain extent while maintaining a comparable prediction accuracy, and realizes a significant enhancement in prediction speed.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) can greatly enhance the capabilities of IoT networks by providing massive connectivity and timely computation. The paper presents a model and analysis of the performance for a large-scale grant-free (GF) SCMA aided MEC network. Firstly, stochastic geometry is used to derive closed-form solutions for offloading probability and SCMA ergodic rate. Then, the impact of SCMA on task completion time and energy cost in MEC networks is studied using queueing theory. Simulation results verify the validity of the theoretical expression and demonstrate that SCMA has advantages over orthogonal multiple access (OMA) in improving the offloading probability and ergodic rate, and reducing task latency and energy cost.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) are two promising technologies for future Internet of Things (IoT) networks. SCMA enables large-scale connections, while MEC brings computing resources closer to user devices, resulting in faster response time and improved user experiences through task offloading. In this article, we investigate a large-scale grant-free (GF) SCMA enhanced MEC network. First, we propose the offloading protocol for the GF-SCMA enhanced MEC framework and describe the task offloading process using GF-SCMA in detail. Then, we model and analyze the performance of this network, deriving closed-form solutions for the offloading probability and SCMA ergodic rate using stochastic geometry. Additionally, we apply queueing theory to examine the impact of GF-SCMA on task latency and energy consumption in the MEC network. The accuracy of the theoretical expressions is confirmed by simulation results, demonstrating that SCMA outperforms orthogonal multiple access (OMA) in terms of increasing offloading probability and ergodic rate, as well as reducing task delay and energy consumption. Furthermore, this advantage becomes more pronounced with higher user density and task generation rate. Through parameter comparison, it is seen that increasing the pilot and codebook number of GF-SCMA can improve the performance of the proposed scheme in practical implementations.

The public key encryption with keyword search (PEKS) schemes are mostly applied to small data sets in mail forwarding systems. When retrieving large databases, the typical search mechanism makes them inefficient and impractical. When designing a PEKS scheme, except for remedying the vulnerability of keyword guessing attacks (KGAs), other leakage issues, such as multipattern privacy and forward/backward security are rarely considered, which may lead to information leakage. Moreover, most existing PEKS only consider applications in single-user scenarios, and cannot be directly transferred to multiuser scenarios, which undermines the value of data utilization. To cope with the above concerns, we propose a PEKS scheme based on an inverted index where the bitmap is used to build the index for the first time in PEKS to meet some seemingly conflicting yet desirable characteristics. First, it has high search efficiency under multiwriter and multiuser. Through linear transformation, users quickly retrieve data and control other users’ access to their data without relying on a third party for authentication. Second, we prove its security in an enhanced security model that achieves multipattern privacy and forward and backward security. It can also resist KGA attacks without a designated tester, which makes it more practical. Finally, it can be extended to achieve search result verification. Compare to the scheme (Zhang et al. ICWS 2016), it has absolute advantages in security and computational cost where the search efficiency is improved by two orders of magnitude.

Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the Internet of Things (IoT) networks can enable efficient connectivity and timely computation for resource-limited IoT users. This paper studies the computation rate maximization problem under task deadline constraints in dynamic SCMA-MEC networks. Specifically, we propose a predictive deep Q-network for SCMA resource allocation and computation offloading (PQ-RACO) algorithm for single-cell scenarios, where IoT devices use long short-term memory (LSTM) networks to predict the states and actions of other agents. However, the PQ-RACO algorithm is not scalable for increasing numbers of IoT devices. To address this issue, an improved multi-agent deep Q-network for SCMA resource allocation and computation offloading algorithm (MQ-RACO) is proposed for multi-cell scenarios. The algorithm is a centralized training and decentralized execution (CTDE) multi-agent reinforcement learning (MARL) algorithm with explicit rewards, which is tailored to the special structure of joint rewards. Simulation results demonstrate that the proposed algorithm outperforms several state-of-the-art MARL algorithms and other benchmark schemes in terms of convergence speed and computation rate.

The application of sparse code multiple access (SCMA) to multi-access edge computing (MEC) networks can provide massive connections as well as timely and efficient computation services for resource-constrained Internet of Things (IoT) devices. This paper investigates the maximization of computation rate in SCMA-MEC networks under a dynamic environment. We first formulate an initial optimization problem to maximize the long-term computation rate of IoT devices under task delay constraints. Then, a joint computation offloading and SCMA resource allocation algorithm based on long short-term memory (LSTM) network and dueling deep Q network (DQN) is proposed. Specifically, each IoT device acts as an agent in the algorithm. Since each device can only observe part of the environment state, the LSTM network is used to predict the states of other devices. The computation rate of devices is taken as a reward to conduct action exploration in dueling DQN, and then the near-optimal computation offloading decision, SCMA codebook allocation, and power distribution of IoT users are obtained after training. Numerical simulation results demonstrate that the proposed algorithm can achieve higher computation rate compared with other baseline schemes.

With the development of data outsourcing technology, the data stored by cloud storage servers are exploding. Secure deduplication for encrypted data helps cloud servers reduce storage overhead in the scenario that cloud users outsource their data in ciphertext. To satisfy client-side semantic security, most existing deduplication schemes for encrypted data need trusted third parties. However, trusted third parties are difficult to deploy and may cause potential risks. Therefore, we propose a secure cloud ciphertext deduplication scheme based on Intel SGX. The proposed scheme uses the Enclave security container provided by Intel SGX as the trusted execution environment on the cloud server to replace the trusted third party to perform sensitive operations. At the same time, our scheme simplifies the secure management of the file encryption keys so that the encryption key of the files with the same data can be securely distributed to other owners of the same file without the need for the original uploader online. We prove the security of the proposed scheme and the experiment shows the efficiency of the scheme.

In social networks, the traditional locally optimized overlapping community detection algorithm has a free-rider problem in community extension, which mainly relies on the structure information of nodes but ignores the node attributes. Therefore, in this paper, we redefine community based on theoretical analysis and propose an overlapping community discovery algorithm based on the local interaction model. By fusing node attributes and structural information, we first proposed an improved density peak fast search method to obtain multiple core nodes in the community. Then, according to the interaction range and interaction mode of the core node, we established a local interaction model of the core node, which converts the interaction strength or the number of common attributes between nodes in the network into the change of the distance between nodes. Finally, according to the proposed improved clustering algorithm, we obtain the community where the core node is located and merge the communities with a high degree of overlap. The experimental results show that compared with other similar community discovery algorithms, the proposed method outperforms the state-of-the-art approaches for community detections.

Sparse code multiple access (SCMA) is a kind of code-domain nonorthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multiaccess edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. In this article, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. Specifically, a system utility is first used to calculate the weighted energy consumption and task execution latency. The initial utility minimization problem is nonconvex and then can be subdivided into two tractable subproblems by fixing task offloading decisions, namely, optimal local computing via CPU frequency scheduling and optimal edge computing via the SCMA codebook assignment, subcarrier power allocation, and MEC server computing resources distribution. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed. Moreover, we come up with CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation (CRA) of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and task offloading can achieve a good compromise between time delay and energy consumption for IoT devices. It is demonstrated that the proposed strategy has a remarkable advantage compared to the previous SCMA-MEC schemes.

Wireless communications are increasingly vulnerable to simultaneous jamming and eavesdropping attacks due to the inherent broadcast nature of wireless channels. With this focus, due to the potential of reconfigurable intelligent surface (RIS) in substantially saving power consumption and boosting information security, this paper is the first work to investigate the effect of the RIS-assisted wireless transmitter in improving both the spectrum efficiency and the security of multi-user cellular network. Specifically, with the imperfect angular channel state information (CSI), we aim to address the worst-case sum rate maximization problem by jointly designing the receive decoder at the users, both the digital precoder and the artificial noise (AN) at the base station (BS), and the analog precoder at the RIS, while meeting the minimum achievable rate constraint, the maximum wiretap rate requirement, and the maximum power constraint. To address the non-convexity of the formulated problem, we first propose an alternative optimization (AO) method to obtain an efficient solution. In particular, a heuristic scheme is proposed to convert the imperfect angular CSI into a robust one and facilitate the developing a closed-form solution to the receive decoder. Then, after reformulating the original problem into a tractable one by exploiting the majorization-minimization (MM) method, the digital precoder and AN can be addressed by the quadratically constrained quadratic programming (QCQP), and the RIS-aided analog precoder is solved by the proposed price mechanism-based Riemannian manifold optimization (RMO). To further reduce the computational complexity of the proposed AO method and gain more insights, we develop a low-complexity monotonic optimization algorithm combined with the dual method (MO-dual) to identify the closed-form solution. Numerical simulations using realistic RIS and communication models demonstrate the superiority and validity of our proposed schemes over the existing benchmark schemes.

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.

Abstract Continual memory attacks, inspired by recent realistic physical attacks, have broken many cryptographic schemes that were considered secure in traditional cryptography model. In this paper, we consider the continual memory leakage resilience in public‐key encryption with keyword search scheme (PEKS). We give the definition of continual memory leakage resilience security for PEKS, which allows continual secret key leakage in the trapdoor generation algorithm rather than leakage of trapdoor itself. We believe that the definition is more suitable for practical PEKS scenario. To construct a concrete PEKS scheme secure against continual memory attacks, we firstly obtain a continual master‐key leakage‐resilient anonymous identity‐based encryption (IBE) scheme by applying the generic tool provided by Lewko et al. to a fully secure anonymous IBE scheme that comes from the fully secure anonymous hierarchical identity‐based encryption (HIBE) scheme of De Caro and colleagues. Then, we transform our continual master‐key leakage‐resilient anonymous IBE scheme to a PEKS scheme using the generic Anonymous IBE‐to‐PEKS transformation and prove its continual leakage‐resilient security. Copyright © 2016 John Wiley & Sons, Ltd.