With the rapid expansion of the Internet of Things (IoT) and social-aware networking technologies, a large number of smart terminal devices are interconnected through social relationships. The explosive growth of multimedia data generated by IoT devices imposes substantial transmission pressure and computational burden on internal networks. Existing data compression methods often exhibit degraded reconstruction quality and limited adaptability when handling multi-task dynamic data in IoT-enabled social-aware networks. To address these problems, this paper proposes a diffusion model-based multi-task adaptive data compression method for IoT-enabled social-aware networks. The method constructs the cluster sub-network structure by introducing the six-degree theory of social networks (Six-SNS) and the Nash equilibrium state, then combines the pretrained zero-shot diffusion model (ZSDM) to realize the adaptive optimization of the measurement matrix, and finally designs the diffusion reconstruction mechanism based on the second Stirling number (SSN) to enhance the accuracy of image reconstruction. The experiments are performed on the social topology dataset Gemsec-Facebook and the multimedia image dataset CelebA. Compared with OMP, AMP-Net, DCGAN, and AdaSense, the proposed method performs well in terms of energy consumption, compression ratio, running time, and image visual quality, which provides a feasible solution for the data compression of IoT-enabled social-aware networks and is particularly suitable for emerging applications such as intelligent sensing, social-aware edge computing, and large-scale distributed IoT systems.

With the quantum threat looming, IoT deployments urgently require post-quantum cryptographic solutions that deliver strong security within tight resource limits. Lattice-based schemes, including NTRU-type encryption(IEEE Std 1363.1) and fully homomorphic encryption (FHE), are attractive in this setting because they can combine quantum resistance with relatively low computational overhead. However, accurate security assessment of these schemes is important for parameter selection in resource-constrained IoT deployments. This work addresses the ternary Learning With Errors (LWE) problem that underpins these cryptosystems by adapting cryptographic puncturing to lattice attacks. In this paper, we revisit the randomized dimension reduction (RDR) technique originally proposed by May to assess the security of the original NTRU cryptosystem. More specifically, we study how the LWE sample dimension can be reduced while preserving the effective secret-error search space through an explicit puncturing threshold, replacing ad hoc sample-selection rules with a single distribution-aware criterion. We also study LWE with side-channel hints, using elimination-based linear equation solving to construct the reduced hint lattice more transparently before the final embedding step, which is relevant in settings where devices may be physically exposed to leakage. Experimental results demonstrate 41% threshold embedding-dimension reduction for NTRU-like schemes (n, log2q,h) = (100, 12, 50) and FHE parameters (n, log2q,h, σe) = (128, 14, 12, 3.2). Relative to standard lattice-estimator evaluations, our refined analysis tightens representative security estimates by 1–3 bits: a CKKS/HEAAN-style parameter set (n, q,w) = (1024, 216, 64) with sparse ternary secret is reduced by 3 bits, while NTRU-Prime (n, q,w) = (653, 4621, 288) and LAC (n, q,w) = (512, 251, 128) parameters are reduced by 1 bit. Additional coefficient-hint experiments on Kyber/ML-KEM parameter settings and representative hint counts from May–Nowakowski show that applying RDR after hint elimination can further reduce the estimated BKZ block size in hint-assisted regimes. These refined estimates provide a more concrete basis for parameter assessment in IoT-style settings.

The emergence of federated learning (FL) promotes the rapid development of artificial intelligence technology in Internet of Things (IoT). However, the huge communication cost and consumption of local computing resource in the process of FL restricts its further development and application in IoT. To solve the above problems, this paper proposes an efficient and secure self-learning federated learning method based on chaotic secure clustering. Firstly, the self-learning weights selection strategy is proposed, this strategy divides FL into two stages. In the first stage, the clients randomly select partial weights according to the inactivation parameter, which are encrypted by CKKS homomorphic encryption and uploaded, and the server updates the weights adaptive matrix according to the performance of the global model. In the second stage, the clients select some weights to encrypt and upload according to the weights adaptive matrix and inactivation parameter. Then, to further improve the efficiency of FL, we propose the chaotic secure clustering method to select some clients. The server performs clustering operation before FL. And in the clustering process, we transform and locally-globally scramble the client label information, which ensures the security of the client information in the clustering process. Finally, we use MNIST dataset, fashion-MNIST dataset, GTSRB dataset and CSE-CIC-IDS2018 dataset to simulate non-independent and identically distributed (non-IID) scenarios, and the experimental results show the effectiveness, security and efficiency of the proposed method, and our method provides a new learning paradigm for the further development and application of FL in IoT.

Federated learning (FL) provides a privacy-preserving solution for model training across distributed Internet of Things (IoT) devices. IoT scenarios typically involve highly heterogeneous data, limited computational capacity, and unstable communication links. Non-independent and identically distributed (non-IID) data further exacerbate training difficulties. Common challenges include reduced model generalization, insufficient robustness against abnormal inputs, and performance unfairness among clients. To address these challenges, personalized federated learning with multi-granularity confidence alignment (FedMGCA) is introduced. FedMGCA incorporates a multi-granularity confidence alignment mechanism to calibrate model confidence at the feature, decision, and distribution levels. In addition, a trust-based dynamic aggregation strategy is adopted to reweight client updates based on reliability assessments. The experimental results demonstrate that FedMGCA consistently outperforms existing personalized federated learning algorithms across various benchmarks.

With the widespread deployment of the Internet of Things (IoT) and distributed sensing systems, visual data are frequently transmitted over bandwidth-limited and unreliable links, where compression and noise degradation are inevitable. This poses significant challenges to secure, imperceptible, and reversible image communication. Existing steganographic methods often suffer from limited reversibility, noticeable statistical distortion, and weak resistance to steganalysis under realistic channel conditions. This paper proposes Rivic, a reversible image hiding framework for robust and secure visual communication. Rivic integrates wavelet-domain multi-frequency modeling, hierarchical spectral refinement, multi-attentive correction, and multi-domain synergistic recovery into an end-to-end invertible network, aiming to enhance embedding stability and noise-resilient reconstruction. Experimental results on ImageNet, COCO, and ImageHide demonstrate that Rivic achieves 34–35 dB PSNR and 0.93–0.95 SSIM in the cover–stego task, yielding a 5–7 dB PSNR improvement over the strongest CNN-based baselines. Under various channel degradations, including additive noise and JPEG compression, Rivic consistently maintains reconstruction quality above 30 dB. Moreover, when evaluated against advanced steganalysis networks such as SRNet, Yedroudj-Net, and SiaSteg-Net, Rivic reduces the average detection accuracy to 0.50–0.62, approaching the level of random guessing. These results indicate that Rivic enables high-fidelity, robust, and secure reversible visual communication in IoT sensing and control systems operating under practical transmission constraints.

The integration of smart medical technology has revolutionized personal health management, with devices like smartwatches and smartphones facilitating efficient data collection and analysis. However, the exponential growth in medical data has heightened privacy concerns, underscoring the need for a robust and secure transmission system. Traditional encryption, while effective, is limited by its resource-intensive nature, hindering its application in IoT devices. Our research delves into the application of compressed sensing for medical image privacy, addressing challenges such as high resource consumption, security vulnerabilities, and the need for robust watermarking. We have designed the Lightweight Quantum-resistant Medical Image Transmission (LQRMIT) model, which combines a 3D chaotic system with the Learning With Errors (LWE) theory, offering a key space of up to$2^{700}$, significantly enhancing security. Additionally, we have designed a watermark embedding algorithm compatible with compressed sensing, which ensures high invisibility and high-quality watermark extraction. The model also employs a meaningful image hiding strategy within encrypted transmissions to improve stealth. Extensive experimental and theoretical analysis has validated the effectiveness of our solution, providing a promising approach for secure and efficient medical image transmission in the digital medical era.

Timed signatures are cryptographic primitives that enable senders to predefine the validity period of a signature. Currently, two primary types of timed signatures have been developed. The first type, known as Verifiable Timed Signatures (CCS'2020), implements a delay before a signature becomes effective. The second type is Short-Lived Signatures (ASIACRYPT'2022), which allows for the setting of an expiration time for signatures upon creation. However, certain applications requiring time-sensitive authorization demand both activation and expiration times to be set, a requirement not fulfilled by the existing timed signature schemes. To overcome this limitation, we propose a novel flexible timed signature scheme called Time Interval Signatures (TIS). TIS combines Verifiable Delay Functions and Short-Lived Signatures with our Zero-Knowledge Proof of Product, facilitating the flexible setting of both activation and expiration times for the signature. Building on TIS, we present TimeGuardian, a time-bound NFT rights protocol that enables presetting authorization and revocation periods for NFT usage rights. Experimental results show that TIS achieves signature size reductions of 98.67% and 57.14% compared to existing verifiable timed signature solutions.

With the advent of intelligent technologies, miscellaneous data containing sensitive information are explosively generated and shared. Compressive sensing methods are naturally suitable for such scenarios due to their joint compression and encryption capabilities. However, data users of most existing compressive sensing methods need to reconstruct original images before use, which brings two disadvantages. First, indiscriminately requiring every data user to reconstruct images without considering their exact requirements is neither advisable nor efficient. Second, allowing all data users to reconstruct original images may cause private or confidential information exposure. To address these issues, in this paper, a novel image sharing method is proposed, which realizes efficient multilevel privacy preservation. Specifically, data owners compress the original images with designed measurement matrices through the proposed Tℓ1-B2DLDA algorithm, which outputs dimension-reduced data with the ability to simultaneously support the subsequent classification tasks for level I data users and reconstruction tasks for level II data users. Therefore, low level data users could achieve their goals without obtaining any private or confidential information in the original images. Experiments are conducted to verify the feasibility, performance and robustness of the proposed method. Furthermore, the security of the proposed method is analyzed both theoretically and practically. The source code of the proposed method is publicly available at https://github.com/xchuxiao23/TL1-B2DLDA.

In this paper, we study the physical layer message authentication and encryption scheme for wireless networks, based on the Advanced Encryption Standard(AES)-based authentication encryption scheme. Specifically, we first propose an efficient and feasible authentication encryption scheme built upon the GCM-SIV mode and utilizing the AES algorithm. Secondly, we propose a novel physical layer security scheme that seamlessly integrates precoding-based message authentication and encryption with the AGSP algorithm by strategically leveraging the unique characteristics of wireless channels. By integrating encryption and authentication at the physical layer, our protocol offers a promising approach to scalable and secure communication in the 6G era.

Reinforcement Learning (RL) optimizes Traffic Signal Control (TSC) to reduce congestion and emissions, but real-world TSC systems face challenges like adversarial attacks and missing data, leading to incorrect signal decisions and increased congestion. Existing methods, limited to offline data predictions, address only one issue and fail to meet TSC's dynamic, real-time needs. We propose RobustLight, a novel framework with an enhanced, plug-and-play diffusion model to improve TSC robustness against noise, missing data, and complex patterns by restoring attacked data. RobustLight integrates two algorithms to recover original data states without altering existing TSC platforms. Using a dynamic state infilling algorithm, it trains the diffusion model online. Experiments on real-world datasets show RobustLight improves recovery performance by up to 50.43\% compared to baseline scenarios. It effectively counters diverse adversarial attacks and missing data. The relevant datasets and code are available at Github.

ABSTRACT The emergence of federated learning (FL) provides a new learning paradigm for private protection of data in the Internet of Things (IoT). However, it takes a lot of time for the server to obtain a global model with superior performance, which restricts the development of FL in the IoT. Therefore, this paper proposes a hybrid‐update efficient federated learning method based on multi‐teacher knowledge distillation in the Internet of Things. Firstly, considering the local training of each client, we design a data separation method of divide and conquer, which transforms data separation into a many‐objective solution problem with constraints, and the unseparated data is used to train local models to speed up the training of the local model. Then, to alleviate the adverse effects of the above method, we introduce the knowledge distillation technology, and a multi‐teacher model is designed for separated data. The teacher models are trained in advance, and they pass on their respective professional knowledge to the local models during the FL process. In the communication between the clients and the server, we only pass part of the model weights to further improve the overall efficiency of FL. To mitigate the impact of the above process, this paper proposes a hybrid‐update federated learning strategy, which divides the update of the global model into federated aggregation update and generative weights update to improve the performance. Finally, we use the MNIST dataset, fashion‐MNIST dataset, GTSRB dataset, SVHN dataset, and 20 Newsgroups dataset to simulate non‐independent and identically distributed (non‐IID) scenarios, and many experiments are performed to verify the effectiveness of the proposed method. Our method improves the overall efficiency of FL and further promotes the development of the IoT.

Pseudo‐random number generator (PRNG) is a type of algorithm that generates a sequence of random numbers using a mathematical formula, which is widely used in computer science, such as simulation, modeling applications, data encryption, et cetera. The efficiency and security of PRNG are closely related to its output bits at each iteration. Especially, we have recently found that linear congruential generator (LCG) is commonly used as the underlying PRNG in short message service (SMS) app, fast knapsack generator (FKG), and programming languages such as Python, while the quadratic generator plays an important role in Monte Carlo method. Therefore, in this paper, we revisit the security of these two number‐theoretic pseudo‐random generators and obtain the best results for attacking these two kinds of PRNGs up to now. More precisely, we prove that when the mapping function of LCG and the quadratic generator is unknown, if during each iteration, generators only output the most significant bits of v i , one can also recover the seed of PRNG when enough consecutive or nonconsecutive outputs are obtained. The primary tool of our attack is the Coppersmith method which can find small roots on polynomial equations. Our advantage lies in applying the local linearization technique to the polynomial equations to make them simple and easy to solve and applying the analytic combinatorics method to simplify the calculation of solution conditions in the Coppersmith method. Experimental data validate the effectiveness of our work.

In the context of connected vehicles, encrypted image search technologies have gained significant importance. However, existing techniques are plagued by several limitations, including high resource consumption, lack of flexibility, insufficient security evaluation, and absence of hierarchical security mechanisms. These constraints render them inadequate for meeting the diverse resource requirements and multi-level security needs of different devices within the connected vehicle ecosystem. Therefore, improvements are urgently needed to enhance efficiency and security. In light of these issues, we introduce a novel framework designed for the secure retrieval of k-nearest Neighbor (kNN) images, leveraging cloud-based storage. This framework is underpinned by a Self-Adaptive Asymmetric Scalar Product Homomorphic Encryption algorithm (SA-ASPE). It incorporates an adaptive block mechanism to generate a suite of encryption keys tailored for images of diverse dimensions. Additionally, a security hierarchy, facilitated by a recursive split tree, is established to provide a selection of multi-tiered security options suitable for a variety of IoT contexts. To further augment the security of image feature vectors, we have integrated chaotic encryption techniques, which serve to thoroughly randomize these vectors, thereby significantly enhancing their unpredictability. To rigorously assess the robustness of the ASPE protocol against potential security threats, we have devised a comprehensive four-tier attack model coupled with an Amplified Attack (AA) strategy. Ultimately, through an extensive comparative analysis of existing and proposed schemes, we demonstrate that our framework adeptly satisfies the efficiency and security demands of a multitude of IoT device privacy protection scenarios.

The Internet of Things (IoT) technology, through the deployment of sensors and intelligent traffic cameras, facilitates the collection, processing, and analysis of traffic flow, vehicle density, and road conditions. However, traffic image data contains a substantial amount of personal sensitive information. Therefore, implementing hierarchical encryption on images is crucial for preventing the leakage of personal privacy and ensuring data is used in compliance with regulations. Moreover, the rise of quantum computing poses a substantial threat to existing key distribution systems, especially for resource-constrained IoT devices. To address these challenges, this article presents a lightweight hierarchical privacy protection scheme for traffic images that addresses key adaptability, quantum threats, and hierarchical privacy protection. The scheme includes a lattice-based public-key algorithm with updatable public and private keys to ensure quantum-resistant and forward security. It also introduces, for the first time, a hierarchical tree-structured encryption scheme based on the SHA3 hash function and the homomorphic property of compressed sensing, allowing high-permission users to generate keys for low-permission users without compromising the quality of image decryption. Additionally, the scheme includes a compressed sensing public-key encryption algorithm based on chaotic systems and the difficulty of matrix factorization, which supports sampling of images of any size, enhancing adaptability and reducing resource consumption. Tests indicate that this image encryption algorithm can resist various statistical analyses, achieving a high number of pixel change rate of 99.6277%. In summary, our research significantly contributes to the effective safeguarding of personal data security in resource-constrained IoT environments, particularly in the face of potential quantum computing threats.

With the development of content generative large models, spoofed speech can be synthesized more easily. However, the generalization of current cloud voice anti-spoofing detection model is insufficient, especially when facing attacks from unknown speech synthesis algorithms. Meanwhile, voice data is also susceptible to attacks during transmission. Therefore, we design an end-to-end voice anti-spoofing scheme, which can be applied in IoT cloud service. The scheme consists of an encryption transmission module and an anti-spoofing model. The encryption module is designed with post-quantum cryptography and chaos to protect transmission security. In the modle, we propose a new higher-order two-dimensional attentive statistics pooling (H2D-ASP) module to extract and aggregate more attention representations in spectral domain and temporal domain; And we propose a new channel-dependent self attention based graph aggregation (CSA-GA) module, which squeezes and aggregates spectral graphs and temporal graphs. Finally, we conduct experiments on the ASVspoof 5 Challenge deepfake database under the closed condition. The experiments show that the model in our scheme is a better single model which performs minDCF 18.28% better than the baseline model on the evaluation set. Without data augmentation, the model achieves minDCF of 0.2994, 0.3604, 0.581 on the development set, evaluationprog set and evaluation set, respectively. The last two proposed modules improve the vanilla model by 29.75% on evaluationprog set and 18.97% on evaluation set.

Current vulnerability detection methods encounter challenges, such as inadequate feature representation, constrained feature extraction capabilities, and coarse-grained detection. To address these issues, we propose a fine-grained hybrid semantic vulnerability detection framework based on Transformer, named TFHSVul. Initially, the source code is transformed into sequential and graph-based representations to capture multilevel features, thereby solving the problem of insufficient information caused by a single intermediate representation. To enhance feature extraction capabilities, TFHSVul integrates multiscale fusion convolutional neural network, residual graph convolutional network, and pretrained language model into the core architecture, significantly boosting performance. We design a fine-grained detection method based on a self-attention mechanism, achieving statement-level detection to address the issue of coarse detection granularity. In comparison to existing baseline methods on public data sets, TFHSVul achieves a 0.58 improvement in F1 score at the function level compared to the best performing model. Moreover, it demonstrates a 10% enhancement in Top-10 accuracy at the statement-level detection compared to the best performing method.

Designing secure outsourcing schemes enables resource-constrained Internet of Things (IoT) devices to perform highly complex computational tasks. Solving the quadratic congruence problem is one of the core components in the construction of cryptographic algorithms for IoT. Recently, Rangasamy designed an outsourcing scheme for solving quadratic congruence equations. Interestingly, we find that the scheme has the risk of secret information being cracked. We propose two lattice attack algorithms in this article. In the first attack algorithm, we prove that there is a possibility of leaking secret information in the public parameters of the outsourcing scheme. Specifically, by intercepting the parameters transmitted between the client and the server, and combining the ideas from Fermat’s Little Theorem and the Euclidean algorithm, the attacker can obtain multiples of the secret parameter p. Furthermore, after recover the value of p, our attack algorithm is capable of recovering all secret parameters in the quadratic congruence equation. In the second attack algorithm, we note that the authors recommend the randomly chosen value of k to be small in the original outsourcing scheme. However, in this article, we point out that the random value k should not be too small, otherwise, the outsourcing scheme would be wrecked. More precisely, we use the Coppersmith method to provide an approach that can recover all the secret information. Our attack algorithm will succeed once k satisfied$|k| \lt \sqrt {p}$, so in order to ensure the security of the outsourcing scheme, we propose the recommended selection length of the random value k. Finally, we experimentally verified the two proposed attack algorithms.

Homomorphic Encryption (HE) is a kind of algorithm which provides data processing but not data access. Since it was proposed in 1978, as one of the important tools in cryptography, it is broadly used in many scenarios, like privacy protection, cloud computing, federated learning, and so on. Especially in the association rules mining privacy protection schemes, it often used as a key technology to ensure data security. Recently, Li et al. and Rajasekaran et al. introduced a kind of symmetric HE algorithm in their privacy protection scheme. However, in this paper, we find that this symmetric HE algorithm has the possibility to recover its secret key in practical applications. We propose two attacking algorithms based on lattice to recover its secret key SK=(sd,q). The core of our attack is to construct a lattice basis using the transformation relation between ciphertexts so that the short vector in the lattice contains the secret key SK. Then we can use the LLL algorithm to recover the secret key. We prove the feasibility of our attacking algorithms with experiments and the experimental results suggest that all of our algorithms can recover the key within 0.1s. Besides, we also give some improvements for this symmetric HE algorithm so that the new HE algorithm can resist our attacking algorithms.

Time-series anomaly detection is critical to securing the Industrial Internet of Things (IIoT). Although numerous deep learning-based methods have been proposed, these methods fail to consider the interdependencies between different dimensions of the data and often neglect the dynamic changes in these dependencies. Moreover, these methods utilize only the global features from the last layer of the network for anomaly detection. However, local features can capture subtle variations in the data, which are crucial for accurately detecting anomalies. To alleviate these problems, this article proposes a novel framework for detecting time-series anomalies, including four parts, namely, the graph structure learning module, the dynamic graph module, the anomaly scoring module, and the self-distillation. The graph structure learning module generates different graph structures based on the inputs, which will be used in the dynamic graph module. The dynamic graph module employs dynamic graph neural networks to capture the complex relationships within time series from both temporal and spatial dimensions. The anomaly scoring module obtains anomaly scores from predictions and observed values, and the model makes anomaly judgments based on these scores. Additionally, self-distillation enhances model performance by utilizing mutual learning between the teacher and student models, thereby integrating local and global information for better anomaly detection. We carry out a series of experiments on IIoT datasets, which verify the performance of the framework. The experimental results of the proposed method outperform other methods, demonstrating the advantage of our framework.

Deep learning-based compressed sensing techniques have exhibited exceptional prowess in signal reconstruction and data-sharing applications, particularly within the realm of IoT sensor data processing. However, existing methods overlook a critical security vulnerability: the susceptibility of compressed sensing techniques to backdoor attacks during the reconstruction phase, which could pose severe security risks to downstream applications. This study pioneers an investigation into the feasibility of backdoor injection during the reconstruction phase, presenting the stealthy multi-constraint adversarial backdoor network against compressed learning (MACS-BNet) and substantiating its efficacy in subverting downstream classification tasks. MACS-BNet synergistically incorporates detailed sensing enhancement, fortified by local information relative positional encoding (LiRPE), to elevate image reconstruction fidelity. Concurrently, it employs a multi-constrained adversarial optimization that integrates sparsity, amplitude regulation, and spatial smoothness constraints, achieving an optimal trade-off between perturbation imperceptibility and attack efficacy. Consequently, victim models are subtly manipulated to yield outputs consistent with the attacker’s objectives. Extensive empirical evaluations reveal that MACS-BNet consistently surpasses seven cutting-edge attack methodologies across attack success rate, clean sample classification accuracy, and stealthiness under both all-to-one and all-to-all attack paradigms. Specifically, MACS-BNet attains an unparalleled clean classification accuracy of 99.52% and an attack success rate of 99.43% in the all-to-one mode, while simultaneously ensuring high-quality image reconstruction. Furthermore, MACS-BNet exhibits formidable resistance against detection by seven state-of-the-art defense mechanisms, underscoring its superior stealth and robustness.

In the context of vehicular crowdsensing within the Internet of Vehicles (IoV), data aggregation techniques enable the computation and analysis of sensing data to extract valuable insights and improve transmission efficiency. However, sensing data and aggregation results often contain sensitive information about terminal vehicles, posing risks of privacy leakage. Existing privacy-preserving data aggregation schemes typically employ homomorphic encryption or bilinear pairing operations to ensure both privacy protection and integrity verification, which incur significant computational overhead. Moreover, when aggregation nodes are untrusted, it becomes challenging to verify the correctness of the aggregated results. To address these challenges, this paper proposes a lightweight and verifiable multi-dimensional data aggregation privacy-preserving scheme for vehicular crowdsensing. In the data generation phase, a blinding factor is introduced to obfuscate the sensing data, and secret sharing is employed to split the obfuscated data into multiple shares. This approach ensures data privacy and resists collusion attacks among internal vehicles. A lightweight signature aggregation method is integrated to verify multiple digital signatures in a single computation, significantly reducing the computational and communication costs associated with integrity verification. In the data recovery phase, the original data can be restored by removing the blinding factor, eliminating the need for traditional decryption algorithms and thereby reducing computational overhead. In the aggregation result verification phase, a homomorphic commitment mechanism is adopted. Users generate commitments of the obfuscated sensing data and upload them to the blockchain, effectively addressing the issue of untrusted roadside units and traffic management centers in real-world applications and ensuring the correctness of the aggregated results. Experimental results demonstrate that the proposed scheme reduces computational overhead by more than 50% compared to existing methods, exhibiting higher efficiency and better adaptability.

Blockchain technology, known for its decentralization and immutability, has been widely applied across various domains. However, this immutability reveals limitations in adapting to rapidly changing legal environments and preventing malicious misuse. To introduce a degree of flexibility, various transaction-level redactable blockchain solutions have been proposed. Yet, current schemes grant modifiers redaction privileges by providing access to the trapdoor, potentially posing risks of malicious dissemination and abuse of the trapdoor. In this paper, we first propose an RSA-based threshold chameleon hash (TCH) construction, allowing the distribution of the trapdoor among a group of authorities. Building on TCH, we develop a threshold policy-based chameleon hash (TPCH). Compared to the Policy-Based Chameleon Hash (PCH) proposed by Derler at NDSS'19, our TPCH supports authorization without exposing the trapdoor. Furthermore, leveraging TPCH, we introduce a novel transaction-level redactable blockchain (TPRB). TPRB supports decentralized authorization without trapdoor exposure and fine-grained rewriting control. Finally, through implementation and evaluation, we demonstrate the practicality and efficiency of our TCH and TPCH schemes.

Unlearning is challenging in generic learning frameworks with the continuous growth and updates of models exhibiting complex inheritance relationships. This paper presents a novel unlearning framework that enables fully parallel unlearning among models exhibiting inheritance. We use a chronologically Directed Acyclic Graph (DAG) to capture various unlearning scenarios occurring in model inheritance networks. Central to our framework is the Fisher Inheritance Unlearning (FIUn) method, designed to enable efficient parallel unlearning within the DAG. FIUn utilizes the Fisher Information Matrix (FIM) to assess the significance of model parameters for unlearning tasks and adjusts them accordingly. To handle multiple unlearning requests simultaneously, we propose the Merging-FIM (MFIM) function, which consolidates FIMs from multiple upstream models into a unified matrix. This design supports all unlearning scenarios captured by the DAG, enabling one-shot removal of inherited knowledge while significantly reducing computational overhead. Experiments confirm the effectiveness of our unlearning framework. For single-class tasks, it achieves complete unlearning with 0% accuracy for unlearned labels while maintaining 94.53% accuracy for retained labels. For multi-class tasks, the accuracy is 1.07% for unlearned labels and 84.77% for retained labels. Our framework accelerates unlearning by 99% compared to alternative methods.

Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchain is employed to ensure the integrity and traceability of FL, where the need to edit multiple interlinked blockchain records and update all inherited models complicates the process. In this paper, we introduce Blockchained Federated Unlearning (BlockFUL), a novel framework with a dual-chain structure— comprising a live chain and an archive chain—for enabling unlearning capabilities within Blockchained FL. BlockFUL introduces two new unlearning paradigms, i.e., parallel and sequential paradigms, which can be effectively implemented through gradient-ascent-based and re-training-based unlearning methods. These methods enhance the unlearning process across multiple inherited models by enabling efficient consensus operations and reducing computational costs. Our extensive experiments validate that these methods effectively reduce data dependency and operational overhead, thereby boosting the overall performance of unlearning inherited models within BlockFUL on CIFAR-10 and Fashion-MNIST datasets using AlexNet, ResNet18, and MobileNetV2 models.

The immutability of blockchain has exposed its limitations in adapting to rapidly evolving legal requirements and preventing malicious misuse. To address these issues, transaction-level redactable blockchain solutions based on the policy-based chameleon hash (PCH) have been introduced. These solutions allow users to create transactions and encrypt trapdoors under specific attribute policies. However, current transaction-level rewriting schemes face two security challenges: Firstly, transactions encrypted with the invalid trapdoor are difficult to rewrite; Secondly, due to lacking version detection on transactions, malicious modifiers may rollback the version of the transaction to launch a reversion attack. In this paper, we present a resilient and redactable blockchain (RRB) with 2-level rewriting and transaction version detection. Specifically, we propose a new redactable blockchain structure that supports both transaction-level and block-level rewriting. To tackle the invalid trapdoor problem, we propose two protocols: a fine-grained, controllable transaction-level rewriting protocol and a centrally controlled block-level rewriting protocol. Moreover, for the transaction reversion attack, we design a version detection mechanism for RRB by using an accumulator. Through security analysis and performance evaluation, we demonstrate the security and practicality of our RRB scheme.

Time series anomaly detection is critical in domains such as cybersecurity monitoring, network operations, and industrial control systems. Lately, unsupervised anomaly detection methods that utilize contrastive learning have shown promise. However, existing approaches often struggle to model high-dimensional temporal dependencies efficiently and rely on rigid feature-fusion schemes that can inadvertently amplify noise. These factors increase computational overhead and sensitivity to irrelevant signals, hindering the capture of salient patterns. Additionally, the explainability of anomalies detected by these mechanisms is often limited, restricting their application in traceable detection processes and an explicit decision-making basis. In this paper, we propose dual-view experts routing and counterfactual generation for explainable time series anomaly detection (DRCAD), a novel framework that detects anomalies within time series data while providing intuitive and actionable explanations for model predictions. DRCAD uses in-patch and patch-wise perspectives as input views for the contrastive learning model, employing a flattened attention mechanism with lightweight spatial projections and a Patch Mixture of Experts (MoE) layer for adaptive routing and information fusion. It identifies anomalies by expanding the discrepancy between normal and anomalous points in the representation space, subsequently outputting anomaly scores. These anomaly scores guide the generation of counterfactual samples, integrating feature change tendencies with normalized feature impacts to derive a feature importance ranking as the explanation. We evaluate DRCAD on six widely used datasets, observe state-of-the-art (SOTA) performance. Moreover, in the explainability evaluation on SWaT dataset, DRCAD achieves superior realism and sparsity in counterfactual generation compared to existing methods, with top-ranked features closely matching officially documented attack characteristics.

When users access the Internet of Things (IoT) devices, authenticating their identities and permissions is an important measure to ensure system security and achieve access control. Standalone authentication (SAA) is an efficient authentication method because it allows IoT devices to independently finish user authentication, avoiding the reliance on a single authentication center in traditional authentication methods. Attribute-based encryption (ABE) supports fine-grained access control and can be used to realize SAA. In certain SAA scenarios, the underlying ABE scheme should also meet some special requirements of security and function. For example, when a central authority seeks help from multiple proxy authorities (PAs) to share the burden of managing users’ access rights, dishonest PAs may grant access rights to illegal users to gain benefits. This situation where the authority abuses its capability of access rights management is referred to as the key escrow problem in ABE (because for ABE, users’ decryption keys represent their access rights). Therefore, we propose a key-policy ABE (KP-ABE) scheme without key escrow to prevent dishonest authorities from abusing their power. Compared with related schemes, the new scheme can relatively fully solve the key escrow problem, has high access policy expressiveness, and is efficient. These advantages ensure that the proposed scheme can be used to achieve secure and efficient SAA in IoT.

Currently, the popularity of social networks has brought us rich and colorful displays and pleasant experiences. However, social networking is also a double-edged sword. While pleasing us, it also raises the issue of privacy disclosure of social media images. How to protect the privacy of media images has become a significant issue for social networks. When we post large quantities of images on social networks to share our daily lives, sometimes we only want to share them with specific friends or want friends with different permissions to see different image content, which involves hierarchical privacy protection. In particular, when the image to be shared contains multiple privacy-sensitive areas of different levels, and we only want to protect the privacy-sensitive areas rather than the whole image, how to protect each privacy-sensitive area is a major problem. Aiming at the above problems, a multilevel privacy protection scheme for image sharing in social networks based on 2-D compressive sensing is proposed. This scheme has the advantages of compressive sampling, privacy protection and controllable access. In addition, we propose a 2-D projected gradient algorithm with accompanying privacy region decryption (2DPG-APRD) for implementing the hierarchical privacy-preserving function of the proposed scheme. Experimental results show that our scheme has multilevel reconstruction quality, high-security intensity for different authorized users, and can well protect the privacy information of images. Therefore, the proposed scheme suits for many practical multilevel encryption situations.

Privacy preservation and low-cost data processing have become two critical issues in the era of Internet of Things (IoT) due to the widespread deployment of lightweight smart surveillance and sensors. In this article, we propose a multitiered reversible data privacy preservation system based on compressive sensing (CS) and watermarking. The system anonymizes the region of interest (ROI) using an obfuscation matrix while compressing and encrypting the entire document. CS provides the first-tier encryption for data documents, and the obfuscation matrix provides the second-tier encryption for sensitive parts of data documents. The system offers a multitiered privacy protection scheme where restricted-authorized users can only access nonsensitive data while fully authorized users can access the entire document. To implement the reversible elimination of the obfuscation matrix, two watermark embedding methodologies are proposed in the CS domain in order. In both methods, the watermark generated by the obfuscation matrix is embedded in the encrypted CS measurement values, with the first methodology concentrating on the optimal data reconstruction quality and the second methodology working to balance storage space and data restoration quality. Extensive experimental results indicate the superiority of the proposed methodologies over other conventional reversible data privacy preservation schemes.

Wireless sensor networks (WSNs) comprised of resource-limited devices face challenges related to network congestion and security threats. In this regard, this study introduces a groundbreaking approach called P-tensor product Fourier optical speckle-based compressive sensing (PTP-FOSCS) for image encryption. Primarily, to augment the scheme’s sensitivity to plaintext information, the parameters for the scrambling encryption were derived using the original image’s SHA-256 hash. Subsequently, the measurement matrix of the compressive sensing (CS) was constructed by capitalizing on the intrinsic randomness offered by Fourier optical speckle. The incorporation of P-tensor product (PTP) theory played a pivotal role by circumventing the conventional hurdle of dimension matching in matrix multiplication, thereby greatly improving the scheme’s flexibility and reducing its storage burden. Furthermore, the optical image encryption offers expeditious and parallel data processing capabilities, making it suitable for integration with CS for encrypting two images simultaneously to improved security and enhanced efficiency within the encryption system. Ultimately, the ciphertext image was discreetly embedded within a carrier image utilizing information hiding technology, which effectively masked the presence of encrypted information, thereby preventing visual suspicion from potential attackers. Empirical validation and comprehensive data corroborate the feasibility and security of the proposed methodology, which has a total key space of approximately 2572. It effectively withstands BFA, statistical attacks, CPA, among others. Furthermore, the novel measurement matrix significantly reduces data storage requirements and achieves higher-quality image reconstruction compared to classical alternatives applied in CS.

With the rapid development of the Internet of Things (IoT), a substantial number of mobile devices may need to be connected to and communicate with the other devices. IoT smart nodes, which play a key role in these processes, provide not only connectivity but also data security during data collection and transmission. While existing research has considered security issues concerning data collection and transmission to some extent, the inherent vulnerabilities of smart nodes themselves have rarely received adequate attention. Actually, smart nodes can be susceptible to malware attacks, and the fundamental task to protect them is the timely and accurate identification of malware. However, the stealthy nature of malware, the imbalance in its types, and the sharp increase in malware variants make this task challenging. Existing static malware classification methods, which require known malware features, may struggle with a whole new set of malware variants. Dynamic methods plausibly have the potential to identify malware variants but necessitate running malware beforehand. In this article, we propose a deep-learning-based malware classification method designed to help most smart IoT nodes running mainstream operating systems preventing attacks from the malware and its variants. The proposed method incorporates an automatically adjusting threshold mechanism to extract trivial features of malware, refining the classification process even if original features are modified. It also performs well with insufficient malware samples and is compatible with most mainstream convolutional neural network (CNN) models allowing flexible implements. Experiments concerning byte-level variants, operation-code-level variants, and noise-caused variants are conducted. The results show that for the malware files and their variants, the proposed method can accurately accomplish the malware classification tasks. In addition, the proposed method offers a robust solution for the malware classification, which helps to enhance the security of smart nodes against a wide range of malware and its variants. Last but not least, its compatibility with mainstream CNN models and flexibility in deployment make it a practical approach for real-world applications.

Blockchain technology was originally designed to ensure data security and trustworthiness through decentralization and immutability. However, in recent years, the misuse of immutability limits the development of blockchain. To address this challenge, several redactable blockchain solutions have been proposed. However, existing solutions either struggle to maintain block consistency or compromise the decentralization principles of blockchain. In this paper, we present a novel redactable blockchain to address these issues. Firstly, we propose a decentralized trapdoor verifiable delay function (DTVDF) based on Wesolowski’s verifiable delay function (VDF) scheme (EUROCRYPT’2019), which distributes trapdoor shares among a group of participants. Then, we leverage the proposed DTVDF to construct our redactable blockchain solution (DTRB), where redacting blocks requires consensus from threshold nodes. Moreover, DTRB provides accountability for malicious modifications and supports aggregate verification of redacted blocks, significantly improving the efficiency of our scheme. Through experimental analysis and comparison with existing solutions, our approach demonstrates superior performance.

With the rapid advancement of smart cities, the utilization of digital images has become widespread, particularly in services such as urban traffic management and public space security surveillance. However, the acquisition, transmission and sharing of digital images inevitably raise concerns about privacy disclosure. To address this challenge, we propose a lightweight image encryption scheme based on data hiding and compressive sensing (CS). Specifically, during the CS sampling and compression stages, we employ data-hiding techniques to embed information from the confusion matrix and coordinates of sensitive regions into CS ciphertext, ensuring the secure transmission of encryption keys for sensitive regions. Additionally, our solution can provide personalized access control mechanisms based on the permission levels of different authorized users and offer customized image recovery quality according to their specific requirements. This effectively addresses the potential privacy leakage risks associated with cross-departmental image sharing, ensuring the security of data transmission and sharing processes. Under consistent experimental conditions, our proposed solution demonstrates a minimum 1.5% improvement in reconstruction quality compared to existing methods. In the security analysis, we further demonstrate that the proposed scheme provides differential reconstruction quality and high-security strength for staff members with different permissions. We believe the proposed solution can suit many practical applications in smart cities.

Voice spoofing detection is a technique for enhancing the security of automatic speaker verification system, but the existing research still faces problems such as weak detection capability and expensive computation. To address these problems, this work presents a lightweight voice anti-spoofing method by using improved one-class learning DOC-Softmax and knowledge distillation. The main idea of DOC-Softmax is to learn a feature space where the genuine samples have a compact space and the spoofing samples are parted from the bona fide space by a certain interval. And the dispersion loss is introduced for spoofing samples to cover the whole spoofing space as much as possible. Moreover, a lightweight voice spoofing detection model is designed to speed up inference, and the knowledge distillation is employed to improve representation power of the lightweight model. Without any data augmentation and ensemble learning, a series of experiments are conducted on LA and PA scenarios of the ASVspoof 2019 dataset, and the experimental results indicate that the proposed method performs better than most existing voice anti-spoofing methods.

As one of the crucial features of the blockchain technique, immutability plays the most important role in winning the so-called praise of the “trust machine” for blockchain. However, there are two sides to everything. The property of immutability of blockchain is applied maliciously sometimes, say publishing harmful or even dangerous data and hindering authorities’ law enforcement. To address this issue, authorized redactability of blockchain was introduced to support block modification without lowering the fundamental basis of security and trust that is cherished on the blockchain. During the past years, several techniques of redactable blockchain were proposed, mainly based on the well-known chameleon hashing. Different from existing methodologies, we propose a new redactable blockchain scheme for permissioned settings in this article. We first employ the trapdoor verifiable delay function to attach a time-lapse proof to each block. Moreover, the trapdoor is used to quickly construct a chain fork to redact blocks that are authorized to alter. Our proposal does not need to rollback irrelevant blocks. As a remarkable and unique feature, our proposal realizes the property of strong synchronization of redaction, which means that all nodes in the blockchain will have identical views on the chain even after some blocks are altered. Security analysis shows that the consistency of the chain is guaranteed, and the long-range attack can be resisted effectively. The performance comparison shows that our method is feasible and practical.

Nowadays, the requirements for image data sharing among participant nodes of the Internet of Things (IoT) are constantly emerging. And thanks to the popularity of digital cameras and the development of digital photography technologies, the processes of data sharing commonly concern substantial amounts of data that may contain critical or private information. So, how to design a secure and efficient secret image sharing (SIS) method suitable for IoT apparatuses is attracting ever-increasing attention. With the aim of simultaneously realizing SIS, image data compression, and critical information or privacy protection, this article proposes a multigroup SIS method based on the model of compressed sensing (CS) and chaos theory. In the proposed method, participants of SIS are classified into two groups with different authorization levels. Solely members of full-authorized groups could reconstruct secret images with visible critical sectors. Members of restricted-authorized groups, however, could merely obtain reconstruction results with critical sectors concealed. The CS model is introduced to the proposed method to accomplish image data compression and applications of the chaos theory contribute to the secure data transmission. Experimental simulations and theoretical analyses are performed to discuss the feasibility, flexibility, and security of the proposed method.

The Internet of Things (IoT) is a large-scale network of various sensing devices connected via the Internet to achieve intelligent functions. Efficient secure data transmission is a significant guarantee for specific functions of IoT. In recent years, compressive sensing (CS) has been applied to the field of the IoT and a substantial number of CS-based IoT data transmission schemes have been proposed. While existing IoT data transmission schemes based on CS could mostly accomplish signal sampling, data compression, and encrypted transmission, the goal of privacy protection has rarely been achieved. Based on chaos theory and semi-tensor product CS (STP-CS), this article proposes two novel secure data transmission methods for different scenarios of IoT: 1) multiple concealing method (MC method) and 2) precise concealing method (PC method). Both methods can provide different levels of reconstruction results for different receivers with various authorization levels. The feasibility, storage requirements, robustness, and security of the proposed methods are theoretically analyzed and experimentally simulated. The results show that the proposed methods ensure both the stability and security of data transmission, save storage space of sensors, and flexibly protect the privacy of the content of data transmitted.

The data collected by sensors is streaming data in the Internet of Things (IoT). Although existing deep-learning-based anomaly detection methods generally perform well on static data, they struggle to respond timely to streaming data after distribution changes. However, streaming data suffers from conceptual drift due to the highly dynamic nature of IoT. In network security, concept drift-oriented anomaly detection is a crucial task, because it can adjust the model to adapt to the latest data, and detect attacks in time. Existing streaming anomaly detection methods are confronted with some challenges, including the latency of model updates, the uneven importance of new data, and the self-poisoning due to model self-updates. To tackle the above challenges, we propose a knowledge distillation-based adaptive anomaly detection model toward concept drift, ADTCD. ADTCD transfers the knowledge of the teacher model to the student model and only updates the student model to reduce the delay. We construct an algorithm of dynamically adjusting model parameters, which dynamically adjusts model weights through local inference on new samples, in order to improve the model’s responsiveness to new distribution data, meanwhile solving the problem of uneven importance of new data. In addition, we adopt a one-class support vector-based outlier removal method to tackle the self-poisoning problem. In comprehensive experiments on seven high-dimensional data sets, ADTCD achieves an AUC improvement of 12.46% compared to the state-of-the-art streaming anomaly detection methods. Our future direction will focus on exploring the concept-drift problem using methods beyond autoencoders.

Most of the voice spoofing detection methods are designed for specific kinds of spoofing attacks, synthetic or replay. In practice, however, there is no prior information about these two kinds of spoofing attacks. To this end, this paper proposes a generalized voice spoofing detection method based on integral knowledge amalgamation to detect jointly synthetic attacks and replay attacks. Two amalgamation mechanisms, feature amalgamation and structure amalgamation, are designed from different perspectives, so that the model can generalize better and run fast. Specifically, the feature amalgamation transfers the high-level sematic knowledge from two teacher models to the compact model. The structure amalgamation employs the adversarial learning to ensure the global structure consistency of two teacher models and a student model. In addition, the feature matching loss is introduced to capture the distinctive features of synthetic attacks and replay attacks. We conduct extensive experiments on logical access (LA) scenario and physical access (PA) scenario of ASVspoof 2019 dataset to verify the validity of the proposed method. The experimental results show that compared with the most advanced generalized voice spoofing detection methods, the proposed method achieves a comparable or even better performance. In particular, our method gains the state-of-the-art detection capability on LA scenario. Moreover, our method achieves similar or even outstanding detectability when compared with specialized anti-spoofing methods.

The identity authentication of most applications is based on a symbolic password. However, incidents of password leakage emerge one after another, which brings serious hidden danger to the users’ information security. For decades, various schemes have been proposed to solve the problem of information protection. However, most schemes neglect the timely detection of password leakage. The present paper introduces a password leak detection method based on zero-divisor graph sequences. Specifically, it is to construct an algorithm for generating honeywords with high smoothness. First, we introduce the concept of the zero-divisor graph and construct zero-divisor graph sequences by using the corresponding zero-divisor matrices. Second, the honeywords with high flatness are constructed by using the sequence of zero-divisor graphs. Third, the security analysis verifies the effectiveness of the scheme. Fourth, compared with other honeywords schemes, our scheme has more obvious advantages, in the aspects of honeywords generated flatness, DoS resistance, and storage resources occupied by honeywords.

With the popularity of social networks, a great variety of new social applications have been generated for impromptu group formation and communications. Among those applications, the subgraph matching has become a hot research area in social networks. Due to the huge cost of managing and computing graph data, it may have to outsource the computations to the cloud server. However, the most critical problem is that the cloud server leaks the graph information during the processing of the graph data, and the external attackers modify the graph information during the transmission on the public channel. Thus, confidentiality and authentication have been critical attributes in the subgraph matching query service. In this article, we present an efficient and privacy-preserving subgraph matching scheme with authentication in social networks. Using the proposed scheme, the cloud can accomplish the subgraph matching query process without obtaining any sensitive information about the users. Additionally, we achieve data integrity verification and user authentication. Each receiver can verify if the received messages come from the legal sender and have not been tampered. The detailed security and efficiency analysis show that the proposed scheme not only satisfies security requirements but also achieves high-efficiency in local users, and it is suitable for many practical applications.

Device-to-Device (D2D) communication has now become one of the most promising technologies in wireless communications of the Internet of Things (IoT). In view of the requirements on data security, response speed, signal decryption quality and storage cost of mobile devices in D2D networks, we propose chaotic deep network (CDN) to achieve secure transmission which is swifter, higher quality and lower cost. The proposed scheme consists of a prepositive nonrepetitive training procedure, a well-designed parallel encryption process, and a set of pretrained chaotic deep neural decryption networks. Benefiting from the utilization of deep learning methods, CDN achieves the swift and accurate decryption at a much lower sampling rate, which brings huge dimension reduction of both measurement matrices and ciphertext signals. Also, chaotic initial values and parameters are applied to matrix generation and network training, leading to great time reduction, storage saving and security improvement. In addition, CDN incorporates the frameworks of semitensor product (STP) and block-based image processing (BIP), which not only breaks through the dimension matching limitation of matrix multiplication by using STP but also maintains the parallelizable block-cipher mode of BIP. Proved by experiments, CDN at the sampling ratio of 25% achieves almost the same or even better peak signal to noise ratio compared to other 8 most frequently used methods at that of 50% for the same test images, and obtains better visual effects. When using a large-sized image of$2^{10}\times 2^{10}$pixels for the experiments, CDN is dozens and even hundreds of times faster than those methods, while reduces the size of measurement matrix from a 500-kB level to a 3-kB level, and the size of compressed data to be transmitted can be reduced by more than 50% as well. Besides, the total key space is approximately$10^{93}$. The adjacent pixel correlation is less than 0.01.

Due to wealthy structure and semantic information expressed by a graph, the graph is frequently employed in numerous social applications to show social relationships. Among those important applications, the private graph intersection operation plays an important part in social networks. Because of the high cost of managing graph data and the computational difficulty of graph intersection operation, delegating the computations to the cloud server (CS) is an attractive alternative. However, when the CS is untrusted or compromised by some adversaries, the results that the CS returns can not be guaranteed to be correct. In such cases, it may have serious consequences for the application functionality. In this article, we present an efficient and privacy-preserving verifiable graph intersection scheme with cryptographic accumulators in social networks. Using the proposed scheme, we construct the framework to provide secure verifiable graph intersection operation in an untrusted cloud, and the requester can verify the correctness of the graph intersection result that the CS returns. Additionally, the data owners' graph data privacy and user authentication are well protected. The detailed correctness proof and performance analysis show that the proposed scheme is secure and feasible. Thus, our scheme is appropriate for many practical applications.

Compressed sensing is an emerging theory of signal processing and it has wide applications in many frontier fields. The construction of the measurement matrices is still a central problem in compressed sensing. In this paper, two types of deterministic constructions of binary measurement matrices are presented via unitary geometry. Then, the lower bounds of the spark of unitary geometry measurement matrices are theoretically analyzed, and an asymptotic comparison between unitary geometry measurement matrices and projective geometry measurement matrices is given via the worst-case recovery capability. After that, a clipping-embedding operation is proposed for binary matrices to generate measurement matrices with more sizes, which can strongly extend the applicability of the deterministic binary matrices in practice. Finally, simulation results demonstrate that the performance of our measurement matrices is comparable to, sometimes even better than, that of the corresponding Gaussian random matrices under OMP and BP.

With the rapid development of the Internet of Things technology and the gradual upgrade of communication methods, a new type of telemedicine system encounters a golden opportunity for development. However, lots of portable sensors for medical health have generated a large amount of sensitive data while bringing convenience to people, which makes the original transmission scheme difficult to meet current transmission requirements. Aiming at realizing high efficiency of image transmission and high security of sensitive image data in the telemedicine system, this article proposes a secure and traceable image transmission scheme. The sensitive image is first compressed and encrypted by the optimized semitensor product compressed sensing algorithm. By the perceptual hash algorithm, the image hash value, also called image fingerprint, is generated from the image content to verify the authenticity of the image. All the image fingerprints are merged to form an image fingerprint chain, which provides the traceability of the image authentication. The digital watermarking technology is used to hide the encrypted sensitive image into a meaningful carrier image, which increases the transmission content without enlarging the transmission load. The experimental results show that the proposed image transmission scheme can effectively improve the transmission efficiency, the reconstruction effect, the security, and the authenticity of the image transmission in the telemedicine systems.

Wireless body area networks (WBANs) collect some physiological parameters of the human body. Each sensor uses limited energy to maximize its own life. There are three crucial problems including adaptiveness, energy, and security in WBANs. In order to solve these problems, a flexible and secure data transmission system is proposed in this paper. The proposed scheme is composed of semitensor compressive sensing (CS), hash function, Arnold scrambling, and chaotic scrambling. For the adaptiveness problem, our scheme uses semitensor CS to encrypt multiple signals with different dimensions. The chaotic sequence is applied to generate the semitensor measurement matrix. On the one hand, we only transmit a few chaotic parameters, which reduces the number of data storage and transmission. On the other hand, the size of the measurement matrix is small, and the computation overhead can be reduced. The security is considered by the proposed scheme which combines Arnold scrambling and logistic scrambling to improve the encryption effect. Numerical simulations and security analyses are given to show that our scheme performs well. The total key space is approximately 2420. The absolute value of adjacent pixel correlation is less than 0.004. Traditional CS method stores 524 288 bytes, while the proposed scheme only stores 2048 bytes. When the compression ratio is less than 0.7, the peak signal to noise ratio of our scheme is obviously higher than those of other three schemes.

The dimension matching is a tough problem in the vector and matrix computations. In the traditional mode, there is only one way to calculate the angle between the 1-D plane and the 3-D vector, it is the projection. However, there are a number of lines on the plane, and taking only the projection to represent the plane is kind of a narrow choice. Furthermore, in the matrix multiplication, the dimension restriction is strict. In order to solve these problems, this paper defines a new model called P -tensor product (PTP), which cannot only define the inner product of two vectors with unmatched dimensions but also give a new way to solve the problems in the matrix operations. Aiming at decreasing the large storage space of the random matrix in compressed sensing (CS), the PTP can reconstruct a high-dimensional matrix by using a matrix, which can be chosen as any kind of matrix. Similar with the traditional CS, we analyze some reconstruction conditions of PTP-CS such as, the spark, the coherence, and the restricted isometry property. The theorems proposed in this paper have a broad sense, and they possess a good universality for various tensor product CS methods. The experimental results demonstrate that our PTP-CS model can not only give more choices to the types of Kronecker matrix and decrease the storage space of the traditional CS but also maintain the considerable recovery performance. Besides, the proposed PTP-CS model can improve the signal transmission efficiency in the Internet of Things.

In this paper, we propose a novel general non-negative matrix factorization (general-NMF)-based digital watermarking scheme for copyright protection and integrity authentication of the image content. Specifically, the proposed general-NMF algorithm is able to factorize a matrix C ∈ R+s×tinto a basis matrix A ∈ R+m×nand a coefficient matrix B ∈ R+p×qby removing the dimension-matching constraints required by the conventional NMF, where s = m, n = p, and t = q. In particular, s = m · l/n, t = l/p · q, and the variable l is the least common multiple of n and p. Furthermore, the generator factor of the random matrix and n are used as the keys of the proposed digital watermarking scheme. Experimental results show that the proposed digital watermarking scheme can effectively resist various attacks and tampering.

This paper studies the problems of “security meridian-collateral” and “security confrontation” systematically and refreshes the traditional concept of security. On the one hand, based on the model of “meridians” in Chinese traditional medicine for the first time, this paper proves the following results strictly by the probability method. There is a complete “meridian-collateral diagram” in Internet of things and any finite system, so that any “sickness” of the system can be cured effectively. On the other hand, this paper studies the network attack and defense from the perspective of the information theory. Through mathematical modeling, based on the famous Shannon's coding theorem, the research on the ability problem of attacker and defender is transformed into the research on the channels of attacker and defender. From the perspective of the channel capacity in information theory for the first time, this paper gives the reachable theoretical limitation of the attack ability of hacker and the defend ability of honker precisely.

Abstract With the purpose of ensuring secure communication through wireless environments, authenticated key agreement protocols with user anonymity are widely investigated. Inspired by the semi‐group property of Chebyshev maps and multiple servers in the network environment, Tsai et al. proposed a novel chaotic maps‐based anonymous authenticated key agreement protocol based on multiserver architecture. Unfortunately, we observe that the Tsai et al. protocol falls to key‐compromise impersonation attack, which opens the door for an attacker to launch an offline password‐guessing attack. Moreover, the Tsai et al. protocol also unfortunately violates the session key security. Elaborating on the security of chaotic maps‐based authenticated key agreement, we present an enhanced protocol employing biometrics that attempts to repair the security pitfalls found in Tsai et al. Security analysis shows that the enhanced protocol satisfies more security attributes while retaining the merits of the original protocol. We also present a formal proof of the enhanced protocol with the Burrows–Abadi–Needham logic. The performance of our protocol is evaluated with its predecessor protocols, and the comparative results show that it outperforms the predecessor protocols in terms of better trade‐off between desirable security attributes and computational overhead. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract With the greatest advancement of information technology, mobile communication has become more widespread and prevalent. When a mobile user intends to enjoy the services offered by a remote server, he needs to be authenticated before constructing a session key with the corresponding server. Numerous authentication schemes have been provided with the purpose of validating the legitimacy of a mobile user. Recently, Xieet al.presented a modified two‐factor authenticated key exchange to eliminate the security flaws of Chenet al.Xieet al.claimed that the enhanced design was more secure than the design of Chenet al.Unfortunately, we identified that the proposed scheme by Xieet al.was insecure against user impersonation, insider and trace attacks and did fail to provide verification in login phase. To enhance the security and efficiency, we then proposed an anonymous authenticated key exchange scheme for mobile client‐server environment. We demonstrated that the proposed scheme was immune to many attacks including attacks observed in the scheme of Xieet al.We also use a formal proof, namely Burrows–Abadi–Needham logic, to analyze the proposed scheme. In addition, the proposed scheme possesses a lower computation overheads than the other related schemes. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract A digital signature scheme allows a signer to sign electronic messages using his or her secret key, and any verifier can validate the correctness according to a given verification procedure. Although a variety of lattice‐based signature schemes have been proposed in the past few years, there does not exist a scheme that has short signatures and constant‐size public keys simultaneously. In this paper, we propose a new method for constructing short lattice signatures with constant‐size public keys in the standard model. In our scheme, each signature contains a low‐dimensional lattice vector and the public key only contains three matrices plus a vector. Compared with previous constructions, our scheme is very simple and does not require any complex homomorphic computation. In order for security proof to work, we introduce a new hard lattice problem, called variant small integer solution (Variant‐SIS), and give the security reduction from small integer solution to Variant‐SIS. Then we define a family of hash functions based on the hardness of Variant‐SIS and prove its security properties, including one‐wayness and collision resistance. As a matter of independent interest, the proposed hard problem may be useful in many other lattice‐based cryptographic constructions. Last, the comparison with similar works demonstrates the superiority of our scheme. Copyright © 2016 John Wiley & Sons, Ltd.

With the rapid development of computer networks, multi-server architecture has attracted much attention in many network environments. Moreover, in order to achieve non-repudiation which both passwords and cryptographic keys cannot provide, several password authentication schemes combining a user's biometrics for multi-server environments have been proposed in the past. In 2014, Chuang et al. presented a biometrics-based multi-server authenticated key agreement scheme and declared that their scheme was efficient and secure. Later, Mishra et al. commented that the scheme by Chuang et al. was susceptible to stolen smart card, impersonation and denial of service attacks. To conquer these weaknesses, Mishra et al. presented an efficient biometrics-based multi-server authenticated key agreement scheme using hash functions. However, we prove that the scheme by Mishra et al. is insecure against forgery, server masquerading and lacks perfect forward secrecy. The focus of this paper is to present a robust biometrics and public-key techniques-based authentication scheme, which is a significant enhancement to the scheme recently proposed by Mishra et al. The highlight of our scheme is that it not only conquers the flaws but also is efficient compared with other related authenticated key agreement schemes. Copyright © 2015John Wiley & Sons, Ltd.

ABSTRACT Delay tolerant networks (DTNs) are new emerging technologies aiming to solve communication issues in challenged network environments. In such networks, any real‐time interactive key agreement protocol does not work due to the intermittent connectivity and long time delay in message round trips. In the context of specific applications, such as single hop authentication, manual public key exchange is the most direct method because single hop authentication can be achieved by holding a small part of node's public key. To evaluate how many public keys should be maintained by each node to achieve a high propagation speed while single hop authentication scheme is used, in this paper, we proposed a security message propagation model for DTNs formed by vehicles where the extended graph theory and rumor spreading terminology in complex networks were harnessed. We find that holding 8–10 public keys by each node is optimal. And decay rate threshold is 0.16 under which message can be disseminated throughout the whole network. Copyright © 2011 John Wiley & Sons, Ltd.