With the rapid development of smart grid technologies, ensuring secure and efficient collaboration among distributed grid units has become a significant challenge. However, existing works suffer from the key escrow problem and single points of failure. Moreover, decentralized alternatives often lack efficient mechanisms to prevent rogue nodes from manipulating collaborative decisions. This paper proposes a certificateless multi-signature scheme named MagicCMS, which is based on a magic square structure. By integrating element-based node collaboration through magic squares, MagicCMS ensures signature generation only when authorized node groups meet predefined constraints. This enhances security against rogue nodes. Additionally, the system utilizes a decentralized key management solution leveraging blockchain technology, thereby eliminating single points of failure. A rigorous security analysis demonstrates that MagicCMS resists both Type-I and Type-II adversaries and provides protection against rogue-key attacks. Performance evaluations show significant improvements in signature verification efficiency compared to existing schemes. In large-scale scenarios, verification computational costs are reduced by up to 73%.

Public key authenticated encryption with keyword search (PAEKS) is a cryptographic primitive applicable in cloud storage systems. It empowers cloud servers to conduct searches on encrypted data without decryption while safeguarding against the brute-force attack known as insider-keyword-guessing attacks (IKGAs). In contrast to the pioneering primitive PEKS, which is vulnerable to IKGAs, PAEKS incurs additional computational and communication overhead due to the sender keys' involvement in encryption and trapdoor-generation processes. Although the recent work improves the efficiency of PAEKS by re-encrypting received ciphertexts, the requirement of a fully trusted proxy is rather costly for users to implement in practice. To reduce the economic cost and to keep a high efficiency, we propose a new primitive ofProxy-free Public-key Authenticated Updatable and Searchable Encryption(PF-AUKS). The key concept is to let the cloud server, instead of the proxy, directly convert different-source ciphertexts into a uniform format securely. We propose a concrete PF-AUKS scheme that supports fast search, constant trapdoor generation, and secure ciphertext update. Theoretical evaluation and experimental results illustrate high algorithm running speed and retrieval efficiency. We formally define the security model of PF-AUKS and prove that our scheme is secure under this model.

The widespread deployment of smart grids has brought significant convenience to residential life. However, it also presents key challenges for data aggregation in smart grids.M1: The hierarchical structure of smart grid consumers (e.g., residential, industrial, commercial) requires differentiated allocation strategies to meet varying electricity demands while protecting consumer privacy.M2: The existing methods, such as superincreasing sequence, often face efficiency challenges, particularly when dealing with multidimensional data.M3: Smart meters continuously collect diverse power consumption data containing users' private information, which is vulnerable to tampering or loss, compromising data integrity and impacting power dispatch decisions. To address these challenges, this paper proposes a blockchain-based, efficient, verifiable, and weighted multidimensional data aggregation scheme for smart grids. First, a novel five-layer cloud-chain-assisted multiscenario data security aggregation model is proposed. Second, instead of using superincreasing sequences, we introduce the Chinese Remainder Theorem to process multidimensional data, thereby reducing communication complexity. Additionally, the property of quadratic reciprocity is leveraged to enhance the decryption method of the Paillier cryptosystem, reducing computational overhead. A weighted aggregation function is implemented to accurately aggregate data based on different user attributes. Furthermore, we propose two sample configurations to address distinct scenario requirements. Security analysis and experimental results demonstrate that the proposed scheme meets practical requirements in terms of both security and efficiency.

In recent years, deep learning side-channel analysis (DLSCA) has garnered significant attention, with the choice of model architecture greatly influencing attack efficiency. Currently, convolutional neural networks (CNNs) have become the dominant architecture in the field of side-channel analysis (SCA), and multi-scale CNNs (MCNNs) have gained favor among certain researchers due to their ability to capture information across various scales. However, effectively obtaining multi-scale information from datasets requires the customization of appropriate hyperparameters for each channel, and the hyperparameter tuning process is often time-consuming and labor-intensive. This presents a technical barrier for non-experts or those seeking to simplify their workflow. Such limitations lead researchers to overly rely on fixed hyperparameter models based on specific datasets, overlooking the differences between various data samples, which in turn affects the model’s reusability and generalization capability in broader scenarios. To address these issues, we propose an adaptive MCNN framework based on automated machine learning, named Auto-MCNN. We evaluated the effectiveness of this framework on multiple private and public datasets. To further investigate the variations in the network’s feature extraction capabilities, we employed an improved heatmap visualization method to illustrate the network’s areas of focus. Experimental results demonstrate that the optimized Auto-MCNN model can be more widely applied to the analysis of side-channel leakage traces, significantly enhancing overall analysis efficiency.

As edge applications demand real-time processing with limited bandwidth and energy, traditional communication systems face challenges to meet performance requirements due to the centralized architecture and redundant data transmission. To address these challenges, we propose a UAV-assisted semantic edge computing network that leverages UAV mobility and semantic communication. We formulate a joint optimization problem involving UAV trajectory, data allocation, and semantic extraction to maximize the semantic processing rate. To solve this problem, we develop a hybrid deep deterministic policy gradient (H-DDPG) algorithm that integrates deep reinforcement learning (DRL) with convex optimization via block coordinate descent (BCD), thereby enabling efficient joint decision-making across tightly coupled variables. Furthermore, we propose a hybrid diffusion deep deterministic policy gradient (H-D3PG) algorithm, which incorporates denoising diffusion models into the DRL framework. By addressing the limited adaptability of deterministic strategies, this design enhances policy expressiveness and stability. As a result, the algorithm enables adaptive trajectory control under time-varying semantic tasks and wireless channel conditions in UAV-assisted edge networks. Simulations show that H-D3PG improves the semantic processing rate by up to 38.8% while reducing energy consumption compared to Raw Data Transmission.

Federated Learning (FL) offers promising solutions for deploying AI in wireless networks, allowing resourceconstrained devices to collaboratively train machine learning models, and reducing deployment costs. However, FL faces challenges due to device heterogeneity and unreliable communication links, which extend training time. Unmanned Aerial Vehicles (UAVs), with their flexibility and deployment advantages, have emerged as valuable assets in addressing these limitations by enhancing line-of-sight communication and providing proximal computational resources. This paper proposes a UAV-assisted FL framework that jointly optimizes resource allocation, task loads, and UAV trajectories to minimize FL completion time. Through a block coordinate descent (BCD) approach, our framework addresses the formulated joint optimization problem. Simulation results demonstrate that our proposed framework effectively balances resource allocation and significantly reduces FL completion time compared to benchmark schemes.

As cloud computing gains widespread adoption, cloud storage services have become the primary means of data management for users. Authenticated data structures (ADS) are a novel computational model designed to address data authentication problems in distributed environments. With the growing demand for robust data security, vulnerability detection in storage systems has become a critical area of focus to ensure resilience against potential threats. However, traditional ADS, while ensuring consistency between cloud data and source data, have limitations in handling dynamic data operations on multiple types of files, storage space expansion, and single-point failure issues. To tackle these issues, this paper proposes a blockchain-assisted classifiable data auditing scheme with dynamic operations. First, trapdoor hash functions are used to construct a binary tree. During dynamic data operations, the impact of hash updates is confined to a subset of nodes, ensuring global stability and reducing computational resource consumption. Second, innovative data structures and verification mechanisms are introduced, reducing the risk of single-point failures by decentralizing the dependency on verification paths. Finally, data types are confirmed based on data identifiers, and corresponding path information is recorded, enabling efficient and rapid dynamic operations on specific types of files within multi-source data. Both security analysis and performance assessment demonstrate that BCDAS conducts data auditing for with reliability and efficiency.

In cryptography, side-channel analysis (SCA) is a technique used to recover cryptographic keys by examining the physical leakages that occur during the operation of cryptographic devices. Recent advancements in deep learning (DL) have greatly enhanced the extraction of crucial information from intricate leakage patterns. A considerable amount of research is dedicated to studying the SubByte (SB) operations of the advanced encryption standard (AES). This is because the SB process, which generates numerous transitions between 0s and 1s during encryption, results in significant energy leakage. However, traditional analysis models primarily focus on the initial round of SB operations in AES, which are less effective on mobile terminals where it is difficult to collect enough signals. These models often neglect additional operations and subsequent rounds, thus providing limited insights from small datasets. Consequently, this limitation has a direct impact on the accuracy and efficiency of key recovery. Our study uses$\rho $-test analysis to show that significant leakage occurs not only during the S-box operation but also during the AddRoundKey (AR) phase of AES. To address these challenges, we propose a new SCA method, that is, optimized for small sample sizes. This method includes a new comprehensive round trace labeling algorithm, which simultaneously analyzes the SB and AR stages of each AES round. Additionally, we introduce the peak precise localization algorithm to accurately identify the points of energy leakage during each encryption round. Our experiments, conducted with power and electromagnetic (EM) datasets from the STM32F303 microcontroller, demonstrate that our method can reliably recover keys with as few as 20 traces. These results highlight the enhanced capability of our method in handling the complexities of small sample datasets in cryptographic analysis.

Secure aggregation of medical data enables detailed data analysis and informed medical decision-making in e-health systems, optimizing data resources utilization and enhancing service quality and decision accuracy. However, the collection of large volumes of medical data poses a significant risk of privacy leakage. Most existing privacy-preserving data aggregation schemes focus on additive aggregation of single or multi-dimensional data, which greatly limits their applicability. This article introduces a blockchain-based multifunctional data aggregation (BM-PDA) scheme for e-health systems. First, BM-PDA supports overall aggregation queries of data samples and can compute the maximum and minimum values within these samples. Second, it enables selective data aggregation queries based on various user attributes. Furthermore, analysis shows that integrating these two algorithms protects both user’s private data and attribute data. Performance evaluations indicate that the computational and communication costs are acceptable, demonstrating the scheme’s practical applicability.

Efficient power management in smart grids relies on regularly obtaining the electricity usage of each user. However, the aggregation of the electricity usage data may expose user privacy. At present, most existing solutions aggregate the electricity data of the entire user set, which cannot meet the fine-grained requirements of the control center. Therefore, this paper proposes a verifiable privacy-preserving multisubset dynamic billing data aggregation scheme. Firstly, we divide the electricity data into k consecutive subsets and set dynamic pricing rules, such as pricing within a certain range as q1and exceeding it as q2. Then, the smart meter encrypts the data using the Paillier cryptographic system with the corresponding subset of parameters and uploads them to the aggregation equipment. After the aggregation equipment completes the users’ data aggregation, the ciphertext C is sent to the control center. This process enables the control center to check data integrity and obtain the total number of people, electricity consumption, and costs for different ranges of electricity in a period of time at once, without the need to obtain data for individual users. Analysis and experiments show that this scheme can resist attacks from powerful adversaries on the transmission channel and has practicality and effectiveness.

Integrating the unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) network with the blockchain technology emerges its superiority in the network utilization, differentiated service, and security, which has been regarded as a promising technique for time-critical applications. In this paper, we propose a UAV-assisted MEC network architecture and a comprehensive data processing flow, where the UAVs cooperate with the base station in computation as edge servers and act as blockchain nodes. We formulate an optimization problem that jointly considers UAVs’ position, data offloading, and resource allocation for minimizing the total time consumption of data processing. To address this problem, we decouple it as three tractable subproblems and propose a Block Coordinate Descent (BCD)-based iterative algorithm. In addition, we analyze the task migration and resource allocation problem in computation, and obtain analytical solutions by the Karush-Kuhn-Tucker (KKT) conditions. The simulated results indicate that the proposed algorithm leads to substantial performance gains.

Unmanned aerial vehicles (UAVs) combining with mobile edge computing (MEC) networks have promoted the application of Internet of Things (IoT) devices, providing enhanced coverage with flexible computing services. But the energy consumption of data processing is still a shortage in the UAV-assisted MEC architecture. Motivated by that, we propose a dynamic UAV-assisted MEC network and formulate a problem and jointly optimize association strategies, UAV trajectory, data offloading, and resource distribution for minimizing total energy consumption. To deal with this tricky problem, we devise a dichotomy-based joint iterative optimization algorithm. Specifically, we divide the problem into three sub-problems, solving by the integer programming, successive convex optimization, and dichotomy method. Finally, the simulation consequences prove that the devised network and algorithm significantly reduce total energy consuming.

With global economic integration, transnational trade plays an important role, and maritime transportation is one of the important means of freight transportation. It is of great significance to build a secure and efficient maritime transportation system (MTS). The introduction of Internet of things technology makes MTS more perfect. The IoT-enabled MTS is composed of marine terminals and on-board sensors, land-based data centers and base stations, as well as satellite networks. Many researchers have carried out significant work to aggregate data in MTS. However, because the terrestrial base stations cannot cover most of the sea area, the isolated maritime terminals, those who drive to the area without base station coverage, need the assistance of satellite networks to complete the contact with the data center. In this paper, we propose an attribute based secure data aggregation scheme for isolated IoT-enabled MTS. In the novel scheme, the constant attributes of a maritime terminal are utilized to generate its certification. In addition, on-board sensors are introduced in the system to help aggregate the status and surrounding environment of the maritime terminal. These monitoring data are encrypted by the sensors and transmitted to the data center for the trustworthiness evaluation of the isolated maritime terminal. Besides, the zero-knowledge proof is utilized to confirm the legitimacy of participating users. What's more, the security analysis and the simulation results show that the novel scheme is secure and efficient for IoT-enabled MTS.

The Internet of Things (IoT) has gained rapid development, but due to the limited battery capacity and access capacity, there are many complex problems in the application. In this paper, we consider an air-and-ground cooperative wireless network, which can provide dynamic coverage for sensor equipments (SEs). Jointly considering the dynamic deployment of the aerial base stations (ABSs) and the admission-and-power control of the SEs, we formulate a two time-scale network control problem to minimize the long-term power consumption of all SEs under their individual rate requirement. On large time scales, we propose a particle swarm optimization algorithm (PSOA) to adjust the positions of the ABSs. On small time scales, we devise a joint admission-and-power control algorithm (JACA). Simulation results indicate that the air-and-ground network incorporated with the proposed algorithms can significantly reduce the total power consumption of the SEs compared with the other schemes.

Incompletely predictable vehicular ad hoc networks is a type of networks where vehicles move in a certain range or just in a particular tendency, which is very similar to some circumstances in reality. However, how to route in such type of networks more efficiently according to the node motion characteristics and related historical big data is still an open issue. In this paper, we propose a novel routing protocol named trustworthiness evaluation-based routing protocol (TERP). In our protocol, trustworthiness of each individual is calculated by the cloud depending on the attribute parameters uploaded by the corresponding vehicle. In addition, according to the trustworthiness provided by the cloud, vehicles in the network choose reliable forward nodes and complete the entire route. The analysis shows that our protocol can effectively improve the fairness of the trustworthiness judgement. In the simulation, our protocol has a good performance in terms of the packet delivery ratio, normalized routing overhead and average end-to-end delay.

In this paper, we focus on the energy saving problem for the wireless sensor networks (WSNs). Specifically, we propose a UAV-assisted wireless network architecture, where the cell-edge sensor devices (SDs) can access the aerial access points (AAPs) instead of the terrestrial access point (TAP). Since the transmitter-to-receiver distance is shortened and the ground-to-air channel is usually line-of-sight, the SDs can use lower power to transmit data and thereby prolong their lifetime. To fully exploit the potential of the network architecture, we jointly optimize the AAPs' position, channel allocation, and power control to minimize the total transmission power of all SDs. In order to solve the complex joint optimization problem, we reformulate it as three tractable subproblems and use the methods in graph theory to design low-complex algorithms. Simulation results indicate that the proposed network architecture greatly outperforms the traditional WSNs, and the proposed algorithms can further reduce the total power consumption.

The delay of vehicle emergency has led to many serious consequences. A series of studies has been carried out in the field of information security in vehicularad hocnetworks (VANETs). However, open issues such as the authentication of emergency vehicle (EV) avoidance are remaining unsolved. In this article, we propose a novel lightweight authentication protocol to avoid EVs in VANETs. In our protocol, after completing the first mutual authentication with the nearest roadside unit (RSU), EV can complete the mutual identity authentication with the subsequent RSUs without repeating cumbersome calculations. Additionally, EV is required to verify the legitimacy of the driver’s identity when starting to avoid some illegal driving behavior. The RSUs will broadcast avoidance information to ordinary vehicles in their jurisdiction to remind them to clear a temporary emergency lane for EVs in advance. With temporary emergency lanes, emergent mission delays due to traffic congestion could be reduced. The security analysis and efficient analysis prove that our protocol is practical and efficient against attacks, such as impersonation attacks, device theft attacks, reputation attacks, etc.

Summary Vehicular ad hoc networks (VANETs) are a type of network, which have caused widespread concern. Researchers have done many research on security and reliability of information transmission in VANETs. However, the secure transmission of emergent information, such as accident information, remains an open issue. The vehicle‐to‐vehicle authentication scheme for traffic accident information transmission not only needs to ensure the orderly and secure transmission of information but also to ensure efficient and rapid transmission. In this paper, we propose a novel scheme for VANETs named neighborhood trustworthiness‐based vehicle‐to‐vehicle authentication scheme (NTVAS). NTVAS makes reasonable utilization of cloud computing technology to evaluate the trustworthiness of vehicles for message delivery. One way vehicle‐to‐vehicle authentication ensures the efficiency of the scheme. Additionally, the concept of accident location neighborhood trustworthiness has also been innovatively presented for further transmission of accident information. The security and performance analysis indicates that our scheme is secure and efficient with low computational cost.

Internet of Things (IoT) is a research field that has been continuously developed and innovated in recent years and is also an important driving force for the improvement of people’s life in the future. There are lots of scenarios in IoT where we need to collaborate through devices to complete tasks; that is, a device sends data to other devices, and other devices operate on the aid of the data. These transmitted data are often users’ privacy data, such as medical data and grid data. We propose an instant encrypted transmission based security scheme for such scenarios in IoT. The analysis in this paper indicates that our scheme can guarantee the security of users’ data while ensuring rapid transmission and acquisition of instant IoT data.

Cyberspace has become the most popular carrier of information exchange in every corner of our life, which is beneficial for our life in almost all aspects. With the continuous development of science and technology, especially the quantum computer, cyberspace security has become the most critical problem for the Internet in near future. In this paper, we focus on analyzing characteristics of the quantum cryptography and exploring of the advantages of it in the future Internet. It is worth noting that we analyze the quantum key distribution (QKD) protocol in the noise-free channel. Moreover, in order to simulate real situations in the future Internet, we also search the QKD protocol in the noisy channel. The results reflect the unconditional security of quantum cryptography theoretically, which is suitable for the Internet as ever-increasing challenges are inevitable in the future.

Smart mobile devices are one of the core components of the wireless body area networks (WBANs). These devices shoulder the important task of collecting, integrating, and transmitting medical data. When a personal computer collects information from these devices, it needs to authenticate the identity of them. Some effective schemes have been put forward to the device authentication in WBANs. However, few researchers have studied the WBANs device authentication in emergency situations. In this paper, we present a novel system named emergency medical system without the assistance of doctors. Based on the system, we propose an identity‐based fast authentication scheme for smart mobile devices in WBANs. The scheme can shorten the time of device authentication in an emergency to achieve fast authentication. The analysis of this paper proves the security and efficiency of the proposed scheme.

With the popularity of smart devices, content based internet services are growing rapidly. Different from real-time services, the end-to-end delay requirements of the contents are much less stringent. In this paper, we consider the delay-tolerant content-aware delivery in two-tier heterogeneous wireless networks. Taking transmit power consumption into account, we propose a new transmission framework by pushing users from macrocells to small cells at the cost of tolerable delay. We model the tolerable delay of content delivery in a stochastic way by considering users' interests. In this model, we adopt a progressively decreasing interest function which treats the delay as its argument. A modified Black-Scholes model is adopted to model the interest function. Thus, there exists a tradeoff between users' experience and power consumption. We consider power efficiency under a certain level of quality of experience (QoE) to handle this tradeoff. A content-aware transmission scheme with the optimal power efficiency is proposed. Simulation results show that under the maximal power efficiency criterion, about 18% of power consumption is reduced while about 90% of users' interests is preserved.

As an effective solution for indoor coverage and service offloading from the conventional cellular networks, femtocells have attracted a lot of attention in recent years. From the perspective of spectral efficiency, the macrocell base station (MBS) and femtocell base stations (FBSs) are usually deployed in the same spectrum. Then the interference problem has become a key obstruction that limits the network performance. In this paper, we study the spectrum reuse in the two-tier LTE femtocell network. In order to improve the network performance, the FBSs are encouraged to provide services to nearby macrocell users, and the MBS releases a fractional spectrum to the FBSs for avoiding cross-tier interference in return. We model this problem as a Stackelberg game where the MBS acts as a leader and the FBSs as the followers. We define the utilities for the MBS and FBSs as the average throughput and the distortion-rate function, respectively. It is worth noting that in our Stackelberg game model, there is no monetary price for the interaction between the leader and followers, which is the significant distinction from previous works. The optimal strategies of spectrum reuse for both MBS and FBSs are proposed by analyzing the Stackelberg game model. The simulation results show that the proposed spectrum reuse scheme can significantly improve the network performance.

This paper is based on the theory of Finite State Automata (FSA's), models a web service as a FSA, extends WSDL for conceptually describing the behaviors of Web services, and introduces the concept of Temporal Logic of Actions (short for TLA) to describe and specify the behavior of a service in a formal way.