Encryption algorithms are usually applied to images in cloud environment to protect privacy, which will make image retrieval difficult. Existing encrypted image retrieval schemes mainly follow the two-step strategy, which firstly extract features from encrypted images and then use these features to conduct retrieval. However, the feature extraction procedure brings additional computational expense, and the extracted feature will result in privacy leakage. In this paper, we propose a new encrypted image retrieval scheme in end-to-end manner. End-to-end means we combine the feature extraction process and the retrieval process, which can reduce computational complexity. Specifically, images are encrypted by DC coefficients encryption, AC coefficients permutation and block permutation. After encryption, the encrypted images can be directly input into our retrieval model, which is based on Vision Transformer (Vit), without any process to conduct retrieval. What’s more, we propose a new type of data augmentation method named random block selection transformation (RST), which is compatible with the Vit backbone and can improve the model performance. Experiment result shows that our scheme can achieve superior retrieval accuracy than other state-to-the-art encrypted image retrieval schemes, while relatively high security can be retained.

The advancement of cloud-assisted Internet of Things (IoT) has amplified the usability for secure and searchable image retrieval, addressing the growing demand for privacy protection in digital multimedia. Current encrypted image retrieval schemes focus either on search precision or search efficiency, making them less viable for deployment on IoT devices with limited resources. Therefore, in this article, we present a privacy-preserving clustering-based image retrieval (PPCBIR) scheme for IoT environment. First, we employ a convolutional neural networks (CNN) for feature extraction and design an extended k-nearest neighbor (kNN) algorithm to protect the privacy of image features. Then, we build a novel clustering-based hierarchical index tree structure to improve retrieval efficiency without compromising data privacy. Subsequently, a matrix re-encryption technique is implemented to achieve the availability of multiterminal key distribution in IoT. Furthermore, we propose an index merging method that is scalable to index trees constructed by different data owners. Finally, formal security analysis demonstrates that PPCBIR is resistant to various threat models. Extensive experiments using authentic datasets indicate that our proposed scheme is comparable to linear retrieval in search accuracy and outperforms existing state-of-the-art schemes in search efficiency, and demonstrate its practicability in IoT.

Physical layer security is a promising approach to enhancing the security of multi-user networks. However, user interference causes constellation points from different users to overlap, limiting both network reliability and security. To address this, we propose a parallel pulse amplitude modulation (PAM) scheme that ensures constellations are regularly superimposed at the legitimate receiver while appearing chaotic to the eavesdropper. Consequently, the eavesdropper experiences a consistently high bit/symbol error rate, whereas the legitimate receiver maintains a very low error rate. Furthermore, we extend the parallel PAM scheme to both the in-phase and quadrature components of the signal, forming a heterogeneous quadrature amplitude modulation (QAM) scheme. This enhances transmission efficiency while preserving security. We analyze the bit/symbol error rates at both the legitimate receiver and the eavesdropper, deriving a lower bound for the eavesdropper’s error rate. Finally, simulation results validate our theoretical analysis.

Syndrome Trellis Code (STC) stands out as one of the nearly optimal steganographic coding methods to date. Its superior efficiency and performance have garnered significant attention. However, STC has a limitation: it struggles to handle unevenly distributed host signals and messages, preventing it from achieving the theoretical minimum distortion. To address this flaw, we introduce an enhanced STC scheme called Content-Aware STC (CA-STC). In our work, we propose modifying the codebook based on the statistical relationship between host signals and messages. This adjustment aims to reduce overall distortion. Additionally, we introduce a new parameter to strike a balance between complexity and embedding efficiency in the proposed method. Simulation results, including scenarios involving random data, demonstrate that our approach outperforms STC in terms of global distortion, effectively adapting to diverse scenarios. Code available: https://github.com/shx-lyu/CA-STC/.

The rising complexity and cost of training DNN models underscore the need for intellectual property protection. In this regard, DNN watermarking has emerged as a crucial safeguarding technique. This paper introduces FedReverse, a multiparty reversible watermarking method that ensures robust copyright protection and minimal performance impact. FedReverse is reversible, allowing collaborative watermark embedding post-training and enables complete removal with unanimous consent. It is resistant to Known Original Attacks (KOA), making watermark forgery and key inference difficult. Comprehensive simulations with MLP and CNN models on varying parameters show FedReverse’s robustness, reversibility, and minimal accuracy impact.

In this paper, we propose a self-interference assisted cooperative jamming scheme to guarantee the security of wireless communication. Different from the traditional cooperative jamming schemes where artificial noise is designed in one phase (or one time slot), we design artificial noise in multiple phases. Moreover, we innovatively introduce carefully designed self-interference in the transmitted signals, which not only guarantees transmission efficiency, but also further enhances security. We first analyze the number of eavesdropping antennas on the security performance and then obtain the secrecy rate. Finally, simulation results corroborate our theoretical analysis.

Reconfigurable intelligent surfaces (RISs) are recognized with great potential to strengthen wireless security, yet the performance gain largely depends on the deployment location of RISs in the network topology. In this article, we consider the anti-eavesdropping communication established through an RIS at a fixed location, as well as an aerial platform mounting another RIS and a friendly jammer to further improve the secrecy. The aerial RIS helps enhance the legitimate signal and the aerial cooperative jamming is strengthened through the fixed RIS. The security gain with aerial reflection and jamming is further improved with the optimized deployment of the aerial platform. We particularly consider the imperfect channel state information issue and address the worst case secrecy for robust performance. The formulated robust secrecy rate maximization problem is decomposed into two layers, where the inner layer solves for reflection and jamming with robust optimization, and the outer layer tackles the aerial deployment through deep reinforcement learning. Simulation results show the deployment under different network topologies and demonstrate the performance superiority of our proposal in terms of the worst case security provisioning as compared with the baselines.

Due to the openness of wireless communication, information transmitted in the multiple access channel is vulnerable to eavesdropping by illegitimate users. Classic artificial noise-assisted schemes can improve security but are not suitable for power-constrained users. To address this problem, we propose a cross-time interference injection scheme in this paper, which improves security by introducing cross-time self-interference and inter-user interference, and does not require artificial noise. The proposed scheme exploits the properties of the Hadamard matrix and the wireless channels, so that the eavesdropping channel suffers from stronger interference than the legitimate channel. We analyze the security performance when both the legitimate receiver and the eavesdropper exploit the successive interference cancellation (SIC) scheme, including the secrecy sum rate, the secrecy capacity, and the secrecy outage probability. We further investigate the effect of the number of eavesdropper’s antennas, the number of legitimate users, and the number of time slots used by the scheme on the security performance, and obtain the corresponding relationship between these parameters when achieving a positive secrecy rate. Finally, simulation results corroborate our theoretical analysis.

Path planning is critical to realizing a high-performance unmanned aerial vehicle (UAV) crowdsensing system, which can be deployed to carry out large-scale tasks in the physical world, especially in emergency scenarios, such as earthquakes and mudslides. Deep reinforcement learning (DRL) has recently proven its superiority in path design. However, it is often applied under the assumption that the entire status of the target region is available, which is hard to achieve in practice. Instead, efforts should be made to ensure the efficient flight of several UAVs in order to collect data with incomplete observations in specified places. In this work, we set out to create a high-performance UAV crowdsensing system by combining DRL with partial observations. We present a novel DRL-based path-planning algorithm called DRL-PP. Specifically, we integrate an attention mechanism into the actor–critic technique to assist UAV swarm collaboration to collect data. We also design an incentive mechanism to ease the problem of sparse reward. Furthermore, we provide a dilemma detection system to prevent the generation of overlapping flight paths. Experimental results from extensive simulations prove that compared with the state-of-the-art approaches, the proposed DRL-PP can significantly improve the efficiency of data collection.

Conventional hybrid precoding and combining based transceivers require a large number of high-resolution radio frequency (RF) phase shifters (PSs), which impose prohibitive hardware costs and power consumption. To address the above issue, both partially connected RF PSs and low-resolution PSs have been proposed. However, the performance limits of these low-cost designs have not been investigated theoretically. Furthermore, there is room for improvement in their spectral efficiency. To fill this knowledge gap, we derive the mean square error performance discrepancy between an optimal precoder/combiner and the hybrid analog-digital precoder/combiner under the constraint of 1-bit PSs relying on lattice theory. Then, by observing that this performance gap can be reduced by deactivating parts of the PSs whilst improving both the spectral and energy efficiency, we develop an adaptive RF PS connection network. To resolve the associated hybrid precoding and combining problems, we appropriately adapt Babai's algorithm from the lattice decoding literature. Our simulation results demonstrate the superiority of the proposed scheme both in terms of its spectral and energy efficiency.

This paper investigates the secure transmission in multiple access wiretap channels, where multiple legitimate users transmit private information to an intended receiver in the presence of multiple eavesdroppers. In order to improve security, we propose a novel cooperative jamming scheme, in which users do not share channel state information (CSI) but the legitimate channels will not be degraded by the artificial noise. The basic idea is to make each user exploit its own CSI in two slots to design artificial noise, so that the intended receiver can eliminate all the artificial noise but the eavesdroppers cannot. In this process, the interference between users plays a key role to achieve security, because it guarantees that the artificial noise from different users helps each other. We consider the non-collusion and collusion of eavesdroppers and analyze the secrecy performance for both scenarios. We adopt the secrecy sum-rate as the main metric, and show that positive secrecy sum-rate can be achieved by using the proposed scheme. Especially, we observe that when eavesdroppers collude and their additive white Gaussian noise (AWGN) close to zero, the number of users must not be less than twice the number of eavesdroppers to ensure positive secrecy sum-rate. Finally, simulation results are provided to corroborate our theoretical findings.

The non-orthogonal multiple access (NOMA) technology can improve spectral efficiency by introducing tolerable inter-user interferences. However, the applicability of NOMA suffers from co-channel interferences and intentional jamming (CIIJ) signals, which severely degrading the estimation and detection performances of NOMA. To overcome this problem, we propose an interference/jamming-immune receiver design, featured by optimal manifold filtering (OMF) for MIMO-NOMA systems. In particular, we formulate an optimization problem of minimizing mean-squared-error (MSE) of filtered symbols. We verify the optimization problem is non-convex and its solutions of filters lie on the Stiefel manifold. Here, an insightful observation is that the problem can be transformed to be convex if a variable transformation is performed. Following this discovery, we prove that the optimal filtering matrices for symbol estimation form a Grassmann manifold on the matrix space. An explicit expression of signal-to-interference- plus-noise ratio (SINR) under those filters is further presented to show the superiority of our proposed receiver in terms of system spectral efficiency, bit-error-rate (BER), and interference and jamming signal suppression. These results demonstrate the capability of NOMA in combating CIIJ signals, thus offering an effective approach for implementation of practical NOMA systems.

The broadcast nature of wireless communications makes transmission susceptible to eavesdropping. Recently, physical-layer security has been extensively studied to secure the wireless transmissions, but the security is severely compromised when the eavesdropper has the superiority in the channel quality. Toward this issue, we in this paper propose a joint ARQ and network coding method exploiting the characteristics of both the physical layer and the link layer. Specifically, we relate the message to be transmitted in the current slot with the successfully decoded messages in previous slots, where the ARQ is employed at the legitimate side to guarantee the successful transmission and decoding. Once one or more received messages fail to be decoded at the eavesdropper, it further prevents the eavesdropper from decoding the transmissions in later slots and thus the message transmitted currently and later is secured at the legitimate side. Moreover, for the more disadvantageous case that the eavesdropper is geographically closer to the transmitter, we further propose a destination-aided network coding scheme, which secures private information no matter where the eavesdropper is. The closed-form expressions of intercept probability and loss probability of the private information are analytically presented. The simulation results are provided to confirm our theoretical findings.

Physical layer security (PLS) has emerged as a promising technique to guarantee the secrecy of wireless communication against eavesdroppers. Although extensive studies have been devoted to the PLS-based transmission design, the combination of this physical layer technique and network layer mechanisms such as routing still remains an open problem. This paper concentrates on the security issue for the cognitive radio networks. Specifically, we focus on the routing protocol design for the artificial-noise-aided multi-hop multi-antenna secondary communication against randomly distributed eavesdroppers. We first derive the closed-form expression of secure connection probability (SCP) for a single hop link. We optimize the power allocation between the information signal and artificial noise to obtain the maximum SCP of each link. Then a secure routing problem is formulated whose objective is to find the multi-hop path having the highest secrecy throughput. An optimal routing strategy based on a revised Bellman-Ford algorithm is proposed. We also propose a suboptimal strategy based on the Dijkstra algorithm with lower complexity. From numerical results we find that the selected route always makes a detour around the primary user to enhance the secrecy throughput.

Conventional time-division duplex (TDD) orthogonal frequency division multiplexing (TDD-OFDM) system is vulnerable to the pilot spoofing attack as it mainly relies on employing the deterministic pilot to capture the channel state information (CSI). To solve the abovementioned issue, we in this paper propose an independent component analysis (ICA) based channel identification and estimation (ICA-CIE) mechanism which uses randomized pilot to paralyze the pilot spoofing attack. Specifically, we first convert the pilot spoofing attack to the pilot jamming attack by exploiting pilot randomization. Then, an eigenstructure-based detector (EID) is proposed to realize efficient pilot jamming attack detection. In particular, on one hand, Least Square (LS) method is adopted if no jamming attack is detected. On the other hand, if jamming attack is detected, we further design a channelseparation oriented joint approximate diagonalization of eigenmatrices (CS-JADE) algorithm for channel estimation and identification. Therein, an optimization problem is first formulated to optimize the fourth-order statistical information of the observation data, after which a pilot signal reconstruction scheme is further developed to help identify true sub-channels from the optimization output. Simulation results demonstrate that our proposed ICA-CIE mechanism can achieve highly-accurate channel estimation for the legitimate transceiver pair only by using 6 OFDM symbols within the coherence time. Moreover, we also observe that the estimation accuracy of our proposed mechanism is immune to Eve's transmit power, which further verifies superiority of our proposed mechanism.

In this paper, the critical effect of noisy feedback in improving the physical layer security is investigated. Unlike previous works, where the eavesdropper's channel state information (either the instantaneous channel state information or the channel distribution information) is assumed known, a feedback and jamming scheme without any eavesdropper's channel state information is studied, which allows both the source and legal destination to transmit private message and degrade the eavesdropper alternatively in different phase of the transmission. More specifically, the situation in which no power constraint is considered first, and it shows using channel inversion a positive secrecy rate can always be achieved by mixing proper amount of artificial noise in the private signal, and the secrecy rate grows linearly with the power of private message in dB. Then we consider the practical situation with power constraint. Interestingly, it shows that a positive secrecy rate can also be obtained by using truncated channel inversion and selecting proper cutoff value and jamming power. Finally, the numerical results verify our analysis.

This paper studies the secrecy outage probability of hybrid relay selection scheme which switches between full-duplex and half-duplex mode. First, motivated by the fact that either the full-duplex or half duplex has its own disadvantages, i.e., the full- duplex suffers from inherently self-interference even after cancellation by advanced technology while half-duplex cannot receive and transmit data simultaneously, we study the hybrid relay scheme. Then, inspired by the superiority of relay selection scheme, we propose the optimal full-duplex relay selection scheme and optimal hybrid relay selection scheme where the eavesdropper adopts joint decoding. Finally, the simulation results are presented, which show proposed scheme significantly outperform the traditional half-duplex relay selection scheme.

Security and energy efficiency are two critical metrics in many multiuser networks (e.g. M2M networks, sensor networks and ad hoc networks). In this paper, we try to maximize secure energy efficiency (SEE) by allocating power for those User Equipments (UEs) meeting the security transmission requirements, where SEE is defined as the ratio of the total secrecy throughput to the total transmission power in the whole network. Concretely, we propose an efficient algorithm which uses the parametric programming and Difference of Convex (DC) function to solve the optimization problem (i.e. maximize secure energy efficiency). Finally, we compare the secure energy efficiency of our scheme with that of fixed power allocation schemes and show the results through simulations at different system conditions.

Supporting massive access of machine-type devices in a short period is a critical challenge in machine-to-machine (M2M) communications. We in this paper propose a traffic-aware Access Class Barring (ACB) scheme to improve the scalability of M2M networks. Unlike traditional ACB scheme, our proposed scheme aim at dynamically regulating the parameter of access probability, called barring factor, based on network load, thus accommodating much more M2M devices as well as lowering the access delay. To achieve this goal, we first develop a Markov-Chain based traffic-load estimation scheme according to the collision status. Then, we propose a spectrum of functions to control the barring factor varying with the estimated traffic load. Also provided is a set of simulations results, demonstrating that our proposed traffic-aware scheme significantly outperforms the traditional ACB scheme in terms of not only access success probability, but also average access delay.