The digital transformation of educational systems has significantly optimized administrative workflows and enhanced the user experience for educators and learners. However, the accumulation of sensitive personal data on educational websites has made them prime targets for cyber threats. Despite growing awareness of these security challenges, the technical roots of vulnerabilities within such platforms remain insufficiently explored. To address this gap, we introduce LLM-EduAttackGraph, a specialized tool designed to assist in vulnerability detection by leveraging large language models (LLMs). Rather than serving as a fully automated monitoring system, LLM-EduAttackGraph operates as a human-in-the-loop assistant, combining expert knowledge with the analytical capabilities of LLMs to help identify potential penetration paths based on network fingerprint information. Using LLM-EduAttackGraph, we have so far identified 961 penetration vulnerabilities across educational websites in mainland China—a number that continues to grow as analysis progresses. These findings demonstrate the tool’s practical value in augmenting cybersecurity research and efforts. Our in-depth analysis of the discovered vulnerabilities reveals that limited developer experience and a heavy dependence on outsourced website development are key contributing factors. By shedding light on these root causes, our research offers actionable strategies and insights aimed at improving the cybersecurity posture of educational platforms and ensuring the sustainable development of online education. Furthermore, we have compared LLM-EduAttackGraph with several existing large model penetration tools to demonstrate the performance of LLM-EduAttackGraph. Such strengths include its low demand for hardware resources and having undergone empirical verification.

The rapidly expanding Ethereum ecosystem has driven the flourishing of decentralized applications, but has also brought increasingly severe security risks. Ponzi scheme, in particular, pose a grave threat to platform security and user assets by luring investors with promises of high returns. The current detection methods generally suffer from limitations such as insufficient feature extraction, reliance on a single information source, and poor robustness. To address these challenges, this paper proposes a novel Multi-View Multi-Modal Fusion Framework with Large Language Models for Ponzi scheme detection on Ethereum, named MF2LLM. We first model the contract opcode sequence as an opcode chain graph and design a Time-Stamped Graph Encoder (TS-GE) to capture local temporal dependencies and execution flow relationships between opcodes. Concurrently, we construct an opcode semantic hypergraph based on semantic categories and design a Semantic-Weighted Hypergraph Encoder (SW-HGE) to model higher-order co-occurrence patterns and global associative features. Furthermore, we propose the Opcode Sequence Lightweighting (OSL) method, which significantly compresses the length of opcode sequences while preserving core control logic and semantic information. This provides high-quality structured input for information fusion. To this end, we perform multi-modal instruction fusion on multi-source heterogeneous features and employ LoRA to fine-tune LLMs. This enables the model to achieve cross-modal semantic reasoning and behavioural pattern recognition. Through extensive experimental validation on real-world datasets, MF2LLM demonstrates stable and superior detection performance even under conditions of highly imbalanced sample distributions. Compared to existing state-of-the-art approaches, our method outperforms across all metrics, achieving an ACC of 99.43%, Precision of 96.57%, Recall of 97.06%, and an F1-score of 96.81%. The efficiency and practical value of MF2LLM in detecting Ponzi schemes on Ethereum contribute to enhanced security for the decentralized application ecosystem. The codes are publicly available on Github: https://github.com/yemisua/MF2LLM.

Existing speech steganalysis suffers from generalization in dynamic scenarios such as unknown or random Embedding Strengths (EBS). The central challenge is domain mismatch, a problem that remains largely unexplored in speech steganalysis. To fill the gap, this letter proposes a novel multi-source domain adaptation method called Fuzzy Clustering-Based Domain Adaptation (FCDA). First, to enable effective clustering of samples with similar actual EBS, FCDA employs Fuzzy C-Means (FCM) clustering, allowing soft estimation across multiple Embedding Rate (EBR) levels. Second, to enhance the steganographic classification performance, we construct the backbone network integrating a dual-primary classifier with an auxiliary EBR hierarchy classifier, which leverages the strength-sensitive representation. Third, to better realize the training of multi-objective tasks and enhance feature discriminability, the domain-consistent optimization loss is introduced. Experimental results show that FCDA outperforms state-of-the-art steganalysis methods and multi-source domain adaptation methods.

Asymmetric numeral systems (ANS) is a widely used entropy coding method in commercial compressors due to its high performance. Joint compression and encryption techniques can offer reliability and cost-effectiveness for secure Big Data storage. However, existing joint compression-encryption schemes for ANS coding often suffer from either increased storage space requirements or limited security. To address these issues, this paper proposes two ANS-based joint compression-encryption algorithms that provide considerable security with almost no compression loss. The first scheme, based on interval swapping, employs a cryptographically secure ChaCha20 generator to perturb the order of contiguous intervals, thereby introducing controlled randomness into the encoding process. The second scheme, based on interval splitting, discards the conventional assumption of representing each symbol with a single contiguous interval, instead assigning multiple sub-intervals to enhance both security and flexibility. In addition, a sequence of output permutations is applied to further strengthen resistance against attacks. Experimental results show that the proposed methods reduce compression loss by approximately 3.83% compared with existing schemes, while the interval swapping scheme achieves a 46.9% reduction in time cost. Security analysis confirms that the enlarged key space significantly increases robustness against brute-force attacks. These results demonstrate that the proposed approaches effectively balance compression efficiency and encryption strength, offering a lightweight and secure solution for Big Data storage.

As industrial systems increasingly rely on 3D mesh models to bridge the physical and digital domains, ensuring their secure and efficient management has become critical. While thumbnail-preserving encryption (TPE) has successfully balanced security and usability for 2D images, extending this concept to 3D models remains largely unexplored. In this work, a novel distributed trust and visually meaningful secret sharing scheme for 3D mesh models (3D-VMSS) is proposed. The distributed trust mechanism splits the model data among multiple participants, ensuring that no single party possesses sufficient information for reconstruction. The original model can only be reconstructed through collaboration among a predefined threshold of authenticated participants. This approach fundamentally differs from traditional single-key encryption by eliminating single points of failure and enabling flexible access control. The scheme segments vertex coordinates into hierarchical components and applies polynomial secret sharing to ensure confidentiality, generating visually meaningful shares that preserve recognizable geometric features while concealing sensitive details. To ensure integrity and resist collusion attacks, dual authentication mechanisms are incorporated. Furthermore, progressive reconstruction enables different quality levels based on participant collaboration. Experimental results demonstrate the scheme's effectiveness in balancing security and practical usability for distributed 3D model management.

Reversible data hiding (RDH) provides a practical solution for the secure storage and transmission of sensitive data in cloud environments. With the increasing adoption of three-dimensional (3D) mesh models in fields that require high privacy and intellectual property protection, RDH techniques specifically designed for these models have gained considerable attention. However, existing RDH schemes for 3D mesh models often encounter limitations such as a low embedding capacity, low runtime efficiency, or insufficient security. To address these challenges, this paper proposes a novel privacy preservation scheme that integrates the geometric projection strategy with Elliptic Curve Integrated Encryption (ECIE). The geometric projection strategy effectively exploits local geometric regularities within mesh models, thereby enhancing the vertex prediction accuracy. The integration of ECIE into the RDH framework further strengthens security by mitigating the risks associated with symmetric key transmission, providing enhanced protection tailored to customized data. Experimental results demonstrate that compared to state-of-the-art methods, the proposed scheme achieves superior embedding capacity and vertex utilization rate while maintaining perfect reversibility, separable data extraction, and high runtime efficiency.

The existing deep-learning based robust watermarking model generally applies a discriminator to form generative adversarial network (GAN) for increasing the quality of encoded images, and adopts a single encoder to embed watermark. However, GAN training is unstable, and the single encoder cannot fully adjust the watermarking distribution, thus affecting the watermarking performance. To address those limitations, this paper presents the multi-encoder based on conditional diffusion model (CDM) for robust image watermarking, namely, DiffW. To enhance the stability, the multi-encoder structure based on CDM replaces GAN for optimizing the watermarking distribution iteratively. Specifically, the operation of each timestep in the forward and reverse diffusion processes of the CDM is regarded as an encoder to overcome the shortcomings of the single encoder structure. At the training stage, under the guidance of the conditional noisy image, the forward process trains each encoder to fuse the image and watermark to generate high-quality encoded images. During the testing stage, only a small number of trained encoders of the forward process are used, so as to reduce the time complexity. Furthermore, to improve watermarking robustness, the channel attention module (CAM) is designed to extract main watermark features by mining channel correlations for multi-layer fusion, so that watermark can be embedded into imperceptible and texture areas. The experimental results reveal that compared with the existing watermarking model, the proposed DiffW can achieve better results in terms of watermarking invisibility and robustness.

With the growing demand for public data openness, sharing data while preserving data privacy has become a critical challenge. Traditional techniques, such as data anonymization and differential privacy, provide baseline privacy guarantees but face some limitations, including limited generalizability and utility degradation due to inappropriate perturbations. To overcome these limitations, this paper proposes a hybrid diffusion model for generating privacy-preserving tabular data. Unlike single-structure data generation models, our proposed approach integrates differential privacy with two lightweight generative models to effectively balance data privacy and data utility. Specifically, our approach consists of three phases: data preprocessing, privacy protection, and data generation. In the data preprocessing phase, the adaptive techniques are used for data normalization. During the privacy protection phase, Gaussian noise and randomized response mechanisms are applied to enhance data privacy. Finally, in the data generation phase, Gaussian diffusion is used for numerical attributes and multinomial diffusion for categorical attributes, which effectively handles the original data of mixed types. This design enhances both the stability of the generative model and the diversity of the synthetic data. Experiments on six public datasets demonstrate that although our approach incurs only a slight reduction in machine learning utility, measured by classification accuracy, F1 score, and regression R2scores, it greatly improves privacy metrics compared to the state-of-the-art tabular diffusion models.

Reversible data hiding in encrypted images (RDHEI) is a technique that not only allows the cover images can be fully restored without any loss of information after the embedded data has been extracted but also ensures the confidentiality within the cover images. This article proposes an RDHEI scheme combining adaptive ( n , n ) secret image sharing (SIS) manner. The content owner reserves part of the least significant bit plane (LSBP) in cover images by two most significant bit planes (MSBPs) compression using the median edge detector (MED) prediction method. To level up the privacy protection of n cover images, a two‐layer encryption method is utilized to generate n shares, that is, the self‐encryption and cross‐encryption. Moreover, our method can be applied on no matter how many of cover images. The secret data with identification can be concealed by the data hiders into the vacated LSB of their own shares. Through the cooperation of the overall shares, the receiver can retrieve the embedded secret data and recover the cover images. Experiment results reveal the security reliability of our approach and the outstanding performance when compared to some related methods. Also, the approach can be employed in color image domain.

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEIs) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding (RDH) with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the Chinese remainder theorem for polynomials (CRTPs). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach usingxorpreservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, number of pixels change rate and unified average changing intensity. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bits per pixel (bpp) and 2.0656 bpp, respectively.

Since the COVID-19 outbreak, there has been a growing need for contactless healthcare to meet medical diagnosis demands. Electronic health systems using the Internet of Things (IoT) are rapidly advancing, transmitting significant amounts of private medical data online. In telemedicine, where patients are diagnosed remotely, sensitive information, such as patient records, may be embedded into medical images for security purposes. Due to the large file sizes of medical images produced by equipment, compression is essential for fast transmission. To safeguard medical images in telemedicine and address bandwidth limitations, we utilize data hiding techniques and absolute moment block truncation coding (AMBTC) compression to introduce an IoT-driven electronic health protection mechanism. Our mechanism employs diverse methods to compress and embed data across various image blocks. Additionally, it offers a flexible adaptation to meet different application requirements concerning embedding capacity, visual quality, and file size by adjusting thresholds and variants. Compared to alternative methods, our approach delivers superior payload capacity and efficiency while preserving visual fidelity.

Crypto-space reversible data hiding (RDH) has emerged as an effective technique for transmitting secret information over the Internet. However, most existing schemes are designed for uncompressed images, while almost all images are processed and transmitted in compressed formats. There is an urgent need to develop methods for compressed images, such as joint photographic experts group (JPEG). In this article, we propose an RDH in encrypted JPEG images, where the bitstreams of alternating current (AC) coefficients and the secret data are mapped to numbers over Galois field. The obtained numbers are then utilized to conduct a polynomial for secret sharing. By reproduction into secret shares, the AC coefficients and the secret data are secured. In addition, a block sorting strategy is used to reduce image distortion under low data payload. Experimental results demonstrate that the proposed scheme outperforms state-of-the-art methods in embedding capacity while preserving the file size and conforming to the JPEG format.

Pixel value ordering (PVO) is an efficient method for implementing reversible data hiding, which can achieve embedding based on overlapping pixel blocks when combined with the flexible patch moving (FPM) mode, especially the two-dimensional (2D) FPM mode. However, the existing 2D FPM mode, whose pairing way of prediction error is not conducive to generating more pixel blocks available for embedding, and whose movement rules are too inefficient to fully exploit the potential of the PVO, results in wasting many available blocks. Therefore, in this paper, a two-stage embedding mechanism is proposed for the 2D FPM mode, in which the combination of prediction errors is adjusted to improve the possibility of generating available blocks and the two-stage embedding doubles the number of pixel blocks available for embedding. Furthermore, an FPM mode selection is proposed, where four novel 2D FPM modes are designed to efficiently exploit the potential of the PVO according to the different directional gradients. Lastly, a set of efficient 2D mappings is well-designed for multiple histograms to achieve lower embedding distortion. The extensive experimental results show that the proposed method outperforms other state-of-the-art methods in terms of embedding capacity and image fidelity. The average peak signal-to-noise ratio for the Kodak image dataset is as high as 63.62 dB after embedding 10,000 bits.

This article proposes a robust decoupling network named RDNet to provide stable traffic predictions even when perturbations exist in historical data. A decoupling block is designed in the RDNet for dividing traffic data into the invariable component (IC) and variable component (VC). The IC of historical or future data is estimated through the invariable block without historical data and thus would not be perturbed. The variable block is developed to forecast the VC of future data using the VC of historical data. Besides, the robust graph neural network and smoothing loss are designed to reduce the effects of perturbations. The RDNet fuses the obtained IC and VC of future data to produce the predictions, and the invariable and decoupling losses are developed for stabilizing the prediction. The results on six open datasets have demonstrated that the RDNet can achieve a 15.62% average improvement in accuracy compared with the state-of-the-art predictor.

The graph-based traffic forecasting is generally realized on the assumption of sufficient data, which could be impractical in the regions without well-deployed mobile sensors or data-processing facilities. Recent studies have developed a solution with the cross-region transfer learning, i.e. transferring traffic knowledge from the source regions to target ones, whose traffic data and computing resources are limited. Nevertheless, relevant issues, including initialization selection and domain adaptation, have not been effectively tackled in the cross-region graph-based traffic forecasting. This paper proposes the cluster-granularity spatiotemporal transfer (CGSTT), which transfers the cluster-granularity knowledge from the source region to target one for the cross-region graph-based traffic forecasting, as not all source knowledge is positive to the target region. Additionally, the domain adaptation is achieved by the dual alignment consisting of the covariate alignment and label alignment of the source/target data, making the proposed CGSTT adapt to the target region efficiently. The superiority of the proposed method over ten compared baseline methods for both short-term and long-term predictions is demonstrated by the conducted experiments on four tasks, which show that it outperforms the state-of-the-art method by achieving an 8.89% average improvement in forecasting accuracy. The PyTorch implementation of the CGSTT is available athttps://github.com/canyangguo/CGSTT.

Federated Learning (FL) enables collaborative model training across maritime devices without the need to share raw data. However, challenges such as data heterogeneity and unreliable marine communications impede its performance and security. In this work, we propose a Blockchain-based Active Client Selection Strategy for FL in Shipping (BAFLS), which utilizes blockchain technology to create a secure and auditable environment for node registration and parameter exchange. A lightweight consensus algorithm is introduced to dynamically elect aggregation nodes based on residual energy, reputation, and computing power, improving fault tolerance and reducing resource consumption. Based on such, a Top-kactive learning strategy is designed to select the most informative clients, balancing data utility and privacy protection. Security evaluation and analysis demonstrate that BAFLS effectively resists aggregation attacks and privacy inference. Experimentations on FMNIST, HAR, and ShipNetwork10 datasets show that BAFLS achieves up to 2.4% higher accuracy, reduces convergence rounds by up to 44%, and consistently lowers communication overhead compared to the baseline under various degrees of label and feature heterogeneity.

Current Transformer-based image steganography cannot embed data properly without considering the correlation of the cover image and the secret image . In addition, to save computational complexity, spatial-wise Transformer is often used to apply in small spatial windows, which limits the extraction of the global feature. To solve those limitations, we present a channel-wise attention Transformer model for image steganography (CAISFormer), which aims to construct long-range dependencies for identifying inconspicuous positions to embed data. A channel self-attention module (CSAM) is deployed to focus the feature channels suitable for data hiding by establishing channel relationships. Meanwhile, a non-linear enhancement (NLE) layer is employed to enhance the beneficial features while weaken the irrelevant ones. For building feature coupling between the cover image and the secret image, a channel-wise cross attention module (CCAM) is designed to fine-tune cover image features by capturing their cross-dependencies. In addition, for concealing data properly, a global–local aggregation module (GLAM) is deployed to adjust fused features by combining global and local attention, which can focus on inconspicuous and texture regions, respectively. The experimental results demonstrate that CAISFormer obtains PSNR gains of more than 0.36 dB and 0.90 dB for the cover/stego image pair and the secret/recovery image pair, respectively, and the detection ratio is decreased by 3.43%, in single image hiding compared to the state-of-the-art. Moreover, the generalization ability is also proved across a variety of datasets. The code will be made publicly available at https://github.com/YuhangZhouCJY/CAISFormer .

Secret image sharing (SIS) is a technique used to distribute confidential data by dividing it into multiple image shadows. Most of the existing approaches or algorithms protect confidential data by encryption with secret keys. This paper proposes a novel SIS scheme without using any secret key. The secret images are first quantized and encrypted by self-encryption into noisy ones. Then, the encrypted images are mixed into secret shares by cross-encryption. The image shadows are generated by replacing the lower bit-planes of the cover images with the secret shares. In the extraction phase, the receiver can restore the quantized secret images by combinatorial operations of the extracted secret shares. Experimental results show that our method is able to deliver a large amount of data payload with a satisfactory cover image quality. Besides, the computational load is very low since the whole scheme is mostly based on cycling-XOR operations.

To achieve accurate traffic forecasting, previous research has employed inner and outer aggregation for information aggregation, and attention mechanisms for heterogeneous spatiotemporal dependency learning, which results in inefficient model learning. While learning efficiency is critical due to the need for updating frequently the model to alleviate the impact of concept drift, limited work has focused on improving it. For efficient learning and accurate forecasting, this study proposes the dynamic spatiotemporal straight-flow network (DSTSFN). Breaking the aggregation paradigms employing both inner and outer aggregation, which may be redundant, the DSTSFN designs a straight-flow network that employs bipartite graphs to learn directly the dependencies between the source and target nodes for outer aggregation only. Instead of the attention mechanisms, the dynamic graphs/networks, which outdo static ones by possessing time-varying dependencies, are designed in the DSTSFN to distinguish the dependency heterogeneity, making the model relatively streamlined. Additionally, two learning strategies based on respectively the curriculum and transfer learning are developed to further improve the learning efficiency of the DSTSFN. Our study could be the first work designing the learning strategies for the multi-step traffic predictor based on dynamic spatiotemporal graphs. The learning efficiency and forecasting accuracy are demonstrated by experiments, which show that the DSTSFN can outperform not only the state-of-the-art (SOTA) predictor for accuracy by achieving a 2.27% improvement in accuracy and requiring only 8.98% of the average training time, but also the SOTA predictor for efficiency by achieving a 9.26% improvement in accuracy and requiring 91.68% of the average training time.

Reversible data hiding in encrypted 3D models (RDHEM) is an emerging steganography technique, capable of both encrypting the cover model to ensure confidentiality and embedding additional messages for covert communication. However, the embedding capacity provided by recent RDHEM methods is still at a low level. In this paper, an adaptive vertex grouping strategy is proposed, which can divide the vertices in the cover 3D model into groups. Then, the multi-MSB prediction and Huffman coding are exploited to compress the data volume of vertices. Through proper vertex grouping and efficient data compression of the model vertices, the embedding capacity of the RDHEM can be effectively improved. Additionally, two schemes for 3D model encryption are provided. One is based on a secret sharing method over the Galois field and the other leverages the stream cipher technique. Experimental results show that the embedding capacity of the two proposed schemes significantly outperforms state-of-the-art schemes.

In this paper, we propose an end-to-end perceptual robust hashing scheme for video copy detection based on unsupervised learning. Firstly, the spatio-temporal information in videos is effectively fused and condensed into high-dimensional features through a 3D self-attention, multi-scale feature fusion model based on 3D-CNN, in which the Inception block and the 3D self-attention mechanism are integrated. Then, we calculate the correlation distances between the extracted features to differentiate perceptual contents. Based on the similarity relationship, we can dynamically generate the pseudo-labels and exploit them to further guide the model training for video hash generation. In addition, we design the dual constraints to make the hash code obtain satisfactory robustness and discrimination. Extensive experiments demonstrate that the proposed scheme achieves superior performance of copy detection compared with existing schemes and performs well even in the case of untrained manipulations.

In the context of healthcare 4.0, cloud‐based eHealth is a common paradigm, enabling stakeholders to access medical data and interact efficiently. However, it still faces some serious security issues that cannot be ignored. One of the major challenges is the assurance of the integrity of medical data remotely stored in the cloud. To solve this problem, we propose a novel certificateless public auditing for medical data in the cloud (CPAMD), which can achieve efficient batch auditing without complicated certificate management and key escrow. Specifically, in our CPAMD, a new secure certificateless signature method is designed to generate tamper‐proof data block tags; a manageable delegated data outsourcing mechanism is presented to reduce the burden of data maintenance on patients and achieve auditability of outsourcing behavior; and a privacy‐preserving augmented verification strategy is proposed to provide comprehensive auditing of both medical data and its source information without compromising privacy. We perform formal security analysis and comprehensive performance evaluation for CPAMD. The results demonstrate that the presented scheme can provide better auditing security and more comprehensive auditing capabilities while achieving good performance comparable to state‐of‐the‐art ones.

Image covert communication camouflages or conceals sensitive information in regular digital images during transmission to avoid detection or interception. It is widely used in military, intelligence, and law enforcement agencies. Data hiding is a feasible solution for covert communication, which embeds confidential information in cover media while preserving its visual appearance. In this letter, we propose an image reversible data hiding (RDH) scheme based on block regulation, which embeds secret data using pixel permutation. Traditional permutation-based data embedding methods usually suffer from error restoration of the cover image. To solve this problem, we propose a regulation operation to pre-process the image blocks, and thus most of the image blocks are converted into regular blocks. The regulated image blocks can be exploited to embed secret data without restoration error. In addition, we propose an adaptive strategy to generate permutation table dynamically, which further enhances the security level. Experimental results indicate that the proposed scheme provides a good embedding capacity and preserves a high level of image visual quality.

Reversible data hiding in encrypted images (RDHEI) can serve as a technical solution to secure data in applications that rely on cloud storage. The key features of an RDHEI scheme are reversibility, security, and data embedding rate. To enlarge the embedding rate, this paper proposes a novel RDHEI scheme based on the median edge detector (MED) and a new proposed hierarchical block variable length coding (HBVLC) technique. In our scheme, the image owner first predicts the pixel values of the carrier image with MED. Then, the prediction error array is sliced into bit-planes and encoded plane by plane. By leveraging the inherent features of the prediction error bit-planes, the image owner adaptively decomposes a bit-plane into blocks of different hierarchical levels based on its local smoothness and encodes the blocks with a variable length coding method. As a result, the carrier image is efficiently compressed to provide spare room for data embedding. The encoded carrier image is then processed with the conventional steps of an RDHEI technique. Experimental results show that the proposed scheme not only can restore the secret data and the carrier image without loss but also outperforms state-of-the-art methods in the embedding rate for images with various features.

Steganography based on fixed codebook has become one of the most important branches of speech steganography due to its high imperceptibility and having the largest available carrier space. As its countermeasure technique, this paper presents a novel steganalysis method based on separable convolution network (SepSteNet) with dual-stream pyramid enhanced strategy (DPES). Specifically, to better acquire discriminative representations, we design the pulse-aware separable block to capture the pulse correspondence along independent levels of pulse positions, where the pulse-aware excitation module is plugged to avoid noisy clue accumulation by adaptively emphasizing the salient part. Moreover, the global attending block is introduced to enhance correspondence features through calculating global responses at distinct subframes. In addition, to eliminate the negative impact of sample content, DPES is leveraged to incorporate cross-domain coherence features by the inverted connected dual-stream branches. With the original and calibration speech samples, two branches enable the correspondence of two detection feature domains to interact with each other to generate coherence features independent of sample content, thereby improving the detection performance. The performance of the presented method is comprehensively evaluated and compared with the state of the arts. The experimental results demonstrate that the presented method significantly outperforms the existing ones. Furthermore, DPES is shown to be a general enhancement strategy that can effectively improve the performance of the existing deep neural network for speech steganalysis. The source code for this work will be publicly available on GitHub.

The graph convolution network (GCN), whose flexible convolution kernels perfectly adapt to the complex topology of the road network, has gradually dominated the spatiotemporal dependency learning of traffic flow data. Defining and learning the spatiotemporal characteristics and relationships of the traffic network efficiently and accurately, which are the important prerequisites for the success of the GCN, have become one of the most burning research problems in the field of intelligent transportation systems. This paper proposes a fast spatiotemporal learning (FSTL) framework containing the fast spatiotemporal GCN module, which reduces the computational complexity of the spatiotemporal GCN from${\cal O(k^{2})}$to${\mathcal{ O(k)}}$, where$k$is the number of time steps of data learned in each GCN operation. To mine globally and fast the correlations of road node pairs, a correlation analysis based on the normal distribution with the complexity of${\mathcal{ O(N)}}$, where$N$is the number of nodes in the traffic network, is proposed to construct the global correlation matrix. Besides, the multi-scale temporal learning is integrated into the FSTL to overcome the receptive field constraints of the spatiotemporal GCN. The experimental results on four real-world datasets demonstrate that the FSTL achieves 48.88% and 5.26% reductions in the training time and mean absolute error, respectively, compared with the state-of-the-art model.

Wheelset fault detection with high accuracy is challenging due to poor image quality. Specifically, the wheelset images are collected dynamically outdoors and suffer from diffuse reflection and environmental interference. Thus, the images contain light stripe adhesions (light flairs) and local fractures to be inpainted. The existing inpainting models are inapplicable to restore grayscale wheelset images. They are also too heavy to be deployed in an embedded wheelset monitoring equipment. In this paper, we propose a lightweight high-precision inpainting model that consists of a recurrent similarity network with the ghost convolution (RSG-Net) to remove light flairs and repair local fractures. RSG-Net replaces standard Pconv (partial convolutional) layers with soft-coding ones that can improve the feature representational ability. To reduce the influence of the background region features on image restoration, an asymmetrical similarity measure is designed to calculate not only the angle difference between the target and the source feature vectors but also the activation of the source ones. The multi-scale structural similarity (MS-SSIM) loss term is introduced to precisely guide the structural information restoration, such as the stripe edges. Moreover, the ghost convolution is introduced in RSG-Net to realize the model compression that can retain the core features of wheelset images and remove the redundant features. We conduct three groups of experiments that demonstrate the accuracy superiority of the proposed RSG-Net over the baseline methods, and the number of parameters is reduced by about 50%.

With the rapid development of network technology, a variety of new malicious attacks appear while attack methods are constantly updated. As the attackers exploit the vulnerabilities of popular third-party components to invade target websites, further improving the classification accuracy of malicious network traffic is the key to improving the performance of abnormal traffic detection. Existing intrusion detection systems may suffer from incomplete feature extraction and low classification accuracy. Thus, this paper proposes an efficient hybrid parallel deep learning model (HPM) for intrusion detection based on margin learning. First, HPM constructs two parallel CNN architectures and fuses the spatial features obtained through full convolution. Secondly, the temporal information of the fused features is parsed separately using two parallel LSTMs. Finally, the extracted spatial-temporal features are fed into the CosMargin classifier for classification detection after global convolution and global pooling. Besides, this paper proposes an improved traffic feature extraction method, which not only reduces redundant features but also speeds up the convergence speed of the network. In the experiment, our HPM has achieved 99% detection accuracy of each malicious class, ranging from 5%–10% improvement with other models, which demonstrates the superiority of our proposed model.

To prevent the abuse of low-rate speech-based steganography from threatening cyberspace security, the corresponding steganalysis approaches have been developed and received significant attention from research community. However, most existing steganalysis methods assume that steganography methods are known in advance, which in practice is impractical. That is why, in this paper, we present three blind detection schemes suitable for steganography in low-bit-rate speech streams. The first is based on mixed sample data augmentation. It randomly selects a certain proportion of steganographic samples from the sample set of each steganographic method to form a training set together with the original carrier samples for training to enhance the robustness of the model. The second relies on decision fusion where first step is to train a dedicated classification model for each steganography method and then use a majority voting mechanism in the detection stage to fuse the outputs of each model to give the final detection result. Compared to the other two steganalysis schemes, the third one design the detection model based on self-paced ensemble according to the distribution characteristics of speech samples. Its main idea is to fully train multiple base classifiers through multiple iterations as well as under-sampling processes, and organically fuse them to form a powerful ensemble classifier. In each iteration, differing from the traditional ensemble classifier solution, we put more attention to the steganographic samples at the decision boundary for the under-sampling process of the steganography set composed of multiple steganography methods, rather than randomly selecting steganographic samples. The steganographic samples at the decision boundary are searched using the classification hardness given by the ensemble classifier trained in the last iteration, which is more informative and more conducive to improve the performance of base classifiers. The experimental results show that the proposed three schemes can achieve efficient blind detection for low-bit-rate speech-based steganography, and the steganalysis scheme based on the self-paced ensemble has the best performance. Specifically, when the embedding rate is at 30%, the accuracy of the steganalysis scheme based on self-paced ensemble is more than 85%, while the accuracy of the other two steganalysis method is less than 80%. Additionally, the steganalysis scheme based on the self-paced ensemble learning even outperforms dedicated detectors for specific steganographic methods in terms of recall for steganographic sample detection.

With the development of speech steganography technology, steganographers are more and more inclined to realize more secure covert communication by combining a series of steganography methods. Thus, this letter presents a novel multi-encoder network (MENet) to achieve more efficient detection of multiple steganography methods. Differing from the previous work, MENet utilizes multiple private encoders to individually model the private features of each coding element, introduces a shared encoder based on an attention mechanism to fuse multiple private features for achieving better feature representation, and finally exploits a shared decoder to reduce feature dimensionality as well as give predictions. Taking the existing state-of-the-art steganography methods as the detection targets, the performance of the proposed steganalysis method is evaluated comprehensively and compared with the state-of-the-art ones. The experimental results show that the detection performance of MENet is overall better than the existing steganalysis methods, especially with low embedding rates and short speech sample lengths.

Secret sharing is a useful method which divides a secret message into several shares for security. During the recovery procedure, only when sufficient shares are obtained, the secret message can be recovered. This paper proposes two novel reversible data hiding schemes in encrypted image via secret sharing over Galois fields${{GF}}{({p})}$and${{GF}}{(}{{2}^{{8}}}{)}$. The content owner first applies a specific encryption method through block and pixel permutation and Shamir’s secret sharing. Then, the theoretical demonstration is introduced to explain that the generated shares are suitable for data embedding over${{GF}}{({p})}$and${{GF}}{(}{{2}^{{8}}}{)}$. Finally, two embedding algorithms over${{GF}}{({p})}$and${{GF}}{(}{{2}^{{8}}}{)}$are presented, and on the receiver side, with different keys, additional data can be extracted correctly and original image can be recovered losslessly. Experimental results show that our schemes can achieve better rate-distortion performance than some state-of-the-art schemes.

Reversible data hiding with contrast enhancement (RDH-CE) is proposed to aim at improving the contrast of images while embedding data. After deeply analyzing and studying the RDH-CE method proposed by Jafaret al., it is found that there are three main problems in their method. Firstly, their method ignores the fact that the left-bottom neighbors of a pixel contribute to increasing the accuracy of the local-complexity evaluation. Secondly, Jafaret al.’s method employs K-means clustering in combination with one single feature to split pixels into five classes, leading to a weak clustering performance. Finally, Jafaret al.’s method uniformly embedded 1 bit into each pixel irrespective of the local complexity, and thus, the embedding capacity is limited. To this end, an improved RDH-CE method is proposed in this paper. Considering that the complexity evaluation plays a vital role in both contrast enhancement and payload increase, we improve embedding performance by including left-bottom neighbors of a pixel into complexity evaluation. Compared with one single feature in Jafaret al.’s method, we extract multiple features to assist K-means clustering such that a better cluster performance is obtained. In addition, our method provides an adaptive pixel modification strategy based on the local complexity, in which we can adaptively embed 1 or 2 bits into a pixel according to the corresponding complexity. By these three improvements, our method is capable of achieving high capacity while enhancing contrast. The experimental results also show that our method achieves higher accuracy of the complexity evaluation, larger payload, and better local contrast enhancement than those existing RDH-CE related methods.

The Internet of Vehicles (IoV) , with the help of cloud computing, can provide rich and powerful application services for vehicles and drivers by sharing and analysing various IoV data. However, how to ensure the integrity of IoV data with multiple sources and diversity outsourced in the cloud is still an open challenge. To address this concern, this paper first presents an identity-based public auditing scheme for cloud storage of IoV data, which can fully achieve the essential function and security requirements, such as classified auditing, multi-source auditing and privacy protection. Particularly, we design a new authenticated data structure, called data mapping table, to track the distribution of each type of IoV data to ensure fine and rapid audits. Moreover, our scheme can reduce the overheads for both the key management and the generation of block tags. We formally prove the security of the presented scheme and evaluate its performance by comprehensive comparisons with the state-of-the-art schemes designed for traditional scenarios. The theoretical analyses and experimental results demonstrate that our scheme can securely and efficiently realize public auditing for IoV data, and outperforms the previous ones in both the computation and communication overheads in most cases.

In this paper, we present our design of a new, blind, reversible, robust, watermarking scheme for relational databases storing textual and numerical data, which is not only guaranteed to meet the above seven requirements and to provide proof of ownership for the legal owner of a relational database but also does not require the existence of the primary key attribute in the relational database. Extensive analysis and our experimental results showed that the proposed scheme achieves strong robustness under a variety of possible attacks, e.g., deletion attacks, modification attacks, addition attacks, and sorting attacks. In addition, in comparison with six existing schemes, the proposed scheme was more secure and robust, as evidenced by our experimental results and robustness analysis.

Vector quantization (VQ) is a popular digital image compression technique. Its resulting index table can be further compressed using the side match vector quantization (SMVQ). In this research, we propose a reversible data hiding scheme based on the de-clustering rules to embed secret data during SMVQ compression. Referring to differently assigned codebooks, the de-clustering rules are equally applicable to both compressible and uncompressible VQ indices. The proposed scheme can produce a camouflaged VQ index table with a high payload. Besides, our scheme is free from the indicator bit, which is required in the conventional SMVQ. Experimental results are compared with state-of-the-art methods.

Nowadays, we greatly depend on Internet mass transmission, of all kinds of data, including critical information among them. Therefore, secure communication is an important topic. Data hiding can embed critical information into carriers, such as images, videos, and so on. Efficiently embedding information into images is the goal of this research. Image steganography techniques utilise a cover image to hide secret data and produce a stego-image by modifying pixel values. After modification, the stego-image is distorted with respect to the cover image. Vector quantisation (VQ) is a lossy image compression technique. The image recovered from the VQ compressed code has distortion compared to the original image. Based on the VQ image, we can hide secret data by modifying the pixel value in a way that the distortion is compensated. The embedding rate of the proposed scheme is adjustable. Experimental results show that our scheme can achieve a high embedding rate in comparison with related works. For low-quality VQ images, embedding can improve the visual quality of the stego-image at the same time.

Reversible data hiding in encrypted images (RDHEI) is a technology that embeds data directly in the encrypted images without decryption or knowledge of the content of the images. However, it is difficult to vacate room from the encrypted image. This problem limits the embedding capacity of the existing RDHEI methods. In this paper, we propose a new RDHEI method designed to supply large embedding capacity without pre-processing of the original images. Using a block-level encryption scheme, the proposed method partially retains the spatial correlation in the high bit-planes of the encrypted image, while the content of the image is held securely. The data hider can compress the high bit-planes of the encrypted image to vacate high-capacity room using Huffman compression coding. In addition, image recovery and data extraction are separable and error-free at the receiver side. The receiver can retrieve the lossless original image without the embedded data using only an encryption key or by extracting the secret data directly from the encrypted image without decryption using only the data-hiding key. The experimental results and comparison proved that the proposed method performs better than previous methods in terms of embedding capacity and visual quality.

As a commonly used image format, JPEG image is popularly used as the carrier for data hiding. After analyzing the virtue of the exploiting-modification-direction (EMD) scheme, which is designed for the spatial domain, this paper reforms its extraction function and applies it to the non-zero alternating current (AC) coefficients of JPEG images. Besides, a block and coefficient selection strategy is utilised to further improve the image quality and suppress the increase of file size. More specifically, the smooth discrete cosine transform (DCT) blocks with fewer zero AC coefficients are preferentially selected to embed data. The experiments indicate that our scheme achieves a high embedding capacity, meanwhile, the stego-image quality keeps good and the file size preserves well, moreover, our scheme is superior to the state-of-the-art scheme.

In the information age, a digital image is an important media for people’s daily interactions. Looking to maintain the quality of the restored image, how to maximise the compression of images has become a challenging topic. Vector quantisation (VQ) compression is an easy-operating image compression method that can compress images to 1/16th of the original size. Based on VQ compresses, a novel image compression method is proposed in this paper. The proposed scheme compresses the image depending on the result of linear regression prediction which can significantly increase the compression ratio.

In a t , n threshold secret sharing (SS) scheme, whether or not a shareholder set is an authorized set totally depends on the number of shareholders in the set. When the access structure is not threshold, (t,n) threshold SS is not suitable. This paper proposes a new kind of SS named grouped secret sharing (GSS), which is specific multipartite SS. Moreover, in order to implement GSS, we utilize both Lagrange interpolation polynomials and Chinese remainder theorem to design two GSS schemes, respectively. Detailed analysis shows that both GSS schemes are correct and perfect, which means any authorized set can recover the secret while an unauthorized set cannot get any information about the secret.

Information hiding is a technique that conceals private information in a trustable carrier, making it imperceptible to unauthorised people. This technique has been used extensively for secure transmissions of multimedia, such as videos, animations, and images. This study proposes a novel Tetris‐based data hiding scheme to flexibly hide more secret messages while ensuring message security. First, an L Q L Q square lattice Q is selected to determine the maximum embedding capacity, and then it is filled without gaps through rotating and sliding tetrominoes while making the shape of each tetromino different. Secondly, according to the decided Q , the reference matrix and corresponding look‐up table are constructed and then used for secret messages embedding and extraction. In the authors approach, each pixel pair of the original image can be processed to conceal 4‐ or 6‐bit secret messages. The experimental results show that their proposed Tetris‐based scheme has excellent performance, exceeding the performance of some state‐of‐the‐art schemes in both embedding capacity and visual quality. The proposed scheme also provides secure covert communication.

Multisecret sharing schemes have been widely used in the area of information security, such as cloud storage, group authentication, and secure parallel communications. One of the issues for these schemes is to share and recover multisecret from their shareholders. However, the existing works consider the recovery of multisecret only when the correspondences between the secrets and their shares are definite. In this paper, we propose a multisecret sharing scheme to share and recover two secrets among the participants based on the generalized Chinese Remainder Theorem (GCRT), where the multisecret and their shares are unordered. To overcome the leakage of information, we propose an improved scheme including the improved sharing phase and the recovery phase. The improved scheme has not only a more secure performance but also a lower computation complexity. The conditions for recovery failure and success are also explored.

Authentication is one of the most fundamental services in cryptography and information security. Compared with the traditional authentication methods, group authentication allows a group of users to be authenticated at once rather than authenticating each of these users individually. Therefore, it is more desirable in the group oriented environment, such as multicast/conference communications. In this paper, we first demonstrate that a recent group authentication scheme by Chien (Security and Communication Networks, 2017) suffers some security flaws, i.e. an adversary in the asynchronous communication model can pretend to be a legitimate group member without being detected. We then use the Anonymous Veto Networks (AV-net) to patch Chien’s scheme, so that its security can be rigorously proved in a well-defined security model.

As a critical parameter in evaluating the reliability of a multiprocessor system when processors malfunction, the \boldmath h-extra connectivity (h-EC) of a multiprocessor system modeled by a graph G, denoted by κo(h)(G), is an h-extra vertex-cut with minimum cardinality. Both of the h-extra conditional diagnosability (h-ECD) and the t/h-diagnosability of the multiprocessor system are vital to tolerate and diagnose faulty processors. These two parameters rely on the resolving of hEC. For the multiprocessor system based on star graph Sn, we show that the 5-EC κo(5)(Sn) of Sn(n ≥ 5) is 6n - 18. As a by-product, we present a novel proof of κo(2)(Sn) = 3n - 7 (resp., κo(4)(Sn) = 5n - 14) by relaxing the restriction n ≥ 10 (resp., n ≥ 7) to n ≥ 5 (resp., n ≥ 5). Furthermore, we determine that the h-ECD of Sn(n ≥ 5) under the preparata, metze, and chien (PMC) model is (h + 1)n - 2h - 1 for 1 ≤ h ≤ 3 and (h + 1)n - 3h + 2 for 4 ≤ h ≤ 5. In addition, we show that Snis [(h + 1)n - 4h + 2]/h-diagnosable for 4 ≤ h ≤ 5, which extends the result that Snis [(h + 1)n - 3h - 1]/h-diagnosable for 1 ≤ h ≤ 3 by [Zhou et al. “The t/k-diagnosability of star graph networks,” IEEE Trans. Comput., vol. 64, no. 2, pp. 547-555, Feb. 2015].

In future small cell networks, tracking areas (TAs) that are defined for location management will be updated frequently to cope with the massive signalling overhead. In this study, a TA planning method based on spectral clustering is proposed to minimise the network signalling overhead. Firstly, handover and paging statistics are simulated to construct a series of graphs showing user mobility and traffic. Then, the TA planning problem is formulated as a classical graph partitioning problem. Finally, a new TA planning method based on spectral clustering is used to build the new TA plan. Simulation results show that the proposed method can effectively reduce the system location update rate and signalling overhead, and improve the system performance.

Cloud computing has become increasingly popular among individuals and enterprises because of the benefits it provides by outsourcing their data to cloud servers. However, the security of the outsourced data has become a major concern. For privacy concerns, searchable encryption, which supports searching over encrypted data, has been proposed and developed rapidly in secure Boolean search and similarity search. However, different users may have different requirements on their queries, which mean different weighted searches. This problem can be solved perfectly in the plaintext domain, but hard to be addressed over encrypted data. In this study, the authors use locality‐sensitive hashing (LSH) and searchable symmetric encryption (SSE) to deal with a privacy preserving weighted similarity search. In the authors’ scheme, data users can generate a search request and set the weight for each attribute according to their requirements. They treat the LSH values as keywords and mix them into the framework of SSE. They use homomorphic encryption to securely address the weight problem and return the top‐k data without revealing any weight information of data users. They formally analysed the security strength of their scheme. Extensive experiments on actual datasets showed that their scheme is extremely effective and efficient.

Remote data backup technology facilitates data storage for users. However, an attacker may intercept some sensitive data on transfer. To solve this problem, sensitive data should be encrypted before uploading to the remote storage. Thus, protecting the secret encryption key is very important. Liu et al . have designed a scheme to protect the secret key using the secret‐sharing method and multi‐factor authentication. Unfortunately, the authors find some security weaknesses of Liu et al .’s scheme. Liu et al .’s scheme cannot resist offline password guessing attack, the server impersonation attack, the user impersonation attack and an attacker updating password/biometrics attack. They present an enhanced secure data backup scheme using multi‐factor authentication to overcome all above‐mentioned security threats. The user first divided a secret used to encrypt sensitive data into three shares using Shamir's secret sharing. Moreover, then the user uses the own password and biometrics to hide the true shares, and stores the pseudo three shares in the smart card, the laptop and the server, separately. Furthermore, the proposed scheme is illustrated in detail, and they give a security comparison of their scheme with Liu et al .’s scheme and computational costs.

In this study, the authors propose a secret image sharing scheme based on code division multiplexing. The secret data are decoded into n host images to generate n meaningful shares using n encoding codes. Any two of encoding codes are orthogonal to each other. If and only if with n shares together, the secret data can be retrieved successfully, and the host images can be recovered completely. The merits of the proposed scheme are summarised as follows: (i) the meaningful shares generated by the proposed scheme can successfully avoid suspicions by malicious attackers during transmission; (ii) the secret data can be losslessly retrieved and the host images can be recovered completely; (iii) the generated shares can be verified so that the forged shares can be detected; (iv) an arbitrary number of shares can be generated to meet the requirement of practical applications; and (v) moreover, n host images can be either identical or different. The experimental results and analyses show that the proposed scheme can provide high security and outperform state‐of‐the‐art secret sharing schemes.

The emergence of Internet of Things (IoT) brings us the possibility to form a well connected network for ubiquitous sensing, intelligent analysis, and timely actuation, which opens up many innovative applications in our daily life. To secure the communication between sensor nodes, gateway devices and cloud servers, cryptographic algorithms (e.g., digital signature, block cipher, and hash function) are widely used. Although cryptographic algorithms are effective in preventing malicious attacks, they involve heavy computation that may not be executed efficiently in resource constraint sensor nodes. In particular, the authentication of a sensor node is usually performed through a digital signature (e.g., RSA and elliptic curve cryptography), which can be slow when executed on a microcontroller. In this paper, an IoT architecture that offloads the digital signature generation to a nearby signature gateway equipped with graphic processing unit (GPU) accelerator are proposed. The communication process for signature offloading, together with optimized implementation techniques for RSA in signature gateway, are also presented in this paper. We have evaluated two different ways to implement modular exponentiation in RSA, namely residue number system and multiprecision montgomery multiplication (MPMM). The experimental results show that our RSA implementation using MPMM is 10.1% faster than the best RSA implementation in GPU. Our proposed IoT architecture with signature gateway can successfully reduce the burden of sensor nodes to generate signatures, at the same time preserve the ability to authenticate the sensor nodes.

The t,n secret sharing scheme is used to protect the privacy of information by distribution. More specifically, a dealer splits a secret into n shares and distributes them privately to n participants, in such a way that any t or more participants can reconstruct the secret, but no group of fewer than t participants who cooperate can determine it. Many schemes in literature are based on the polynomial interpolation or the Chinese remainder theorem. In this paper, we propose a new solution to the system of congruences different from Chinese remainder theorem and propose a new scheme for t,n secret sharing; its secret reconstruction is based upon Euler’s theorem. Furthermore, our generalized conclusion allows the dealer to refresh the shared secret without changing the original share of the participants.

Blockchain is an emerging technology that promises many exciting applications in various fields, including financial, medical, energy, and logistics management. However, there are still some limitations in the existing blockchain framework that prevents its widespread adoption in the commercial world. One important limitation is the storage requirement, wherein each blockchain node has to store a copy of the distributed ledger. Thus, as the number of transactions increases, this storage requirement grows quadratically, eventually limiting the scalability of a blockchain system. Moreover, the public ledger in a blockchain framework allows anyone in the network to audit the transaction, which may not be favourable in some privacy-sensitive applications. In this paper, a secret-sharing scheme is proposed to reduce the size of the blockchain transactions. Each transaction block is divided into t parts, and the size of each part is 1/t size of transaction block. We use the secret-sharing mechanism to share t parts into n shares. Hence, each node stores not one transaction but one share in the blockchain system. The proposed scheme can eventually reduce the storage cost of a blockchain transaction by 1/t without introducing an additional recovery communication cost; however, robustness is reduced in node failure as a tradeoff. Meanwhile, the proposed scheme was more efficient and secure compared to other state-of-the-art schemes that aim to reduce blockchain storage for industrial big data.

Reversible data hiding (RDH) is a method that allows a cover image to be completely recovered from its corresponding stego image without distortion after the embedded secret messages have been extracted. Prediction-error expansion (PEE), as a classic RDH scheme, has been studied extensively due to its high quality of stego images. Based on prediction errors, threshold values, and the relative distances between each bin and zero bin, we present a bidirectional shift and double-way prediction strategy to solve the multiple embedding problem. Compared with the original algorithm, this scheme only takes a little more time and reduces the PSNR slightly, but it improves the embedding capacity significantly and allows for reversible data hiding. When both threshold values of TH and TH∗ are equal to 2, the average ER value of 108 test images is 1.2 bpp which is ideal for medium data payload. At the same time, the PSNR is above 30 dB, making embedded information visually imperceptible. These data, together with other experimental results, show that the method proposed in this paper has obvious advantages in image quality and embedding capacity.

This article presents a microcontroller unit (MCU) based simplified discrete wavelet transform (Sim-DWT) algorithm that can detect high-voltage spindles (HVSs) in local field potential (LFP) signals. The Sim-DWT algorithm operates in an 8-bit MCU, 8MHz operating clock and 16 sample points of buffers to detect HVSs with a frequency range of 5−15Hz. The requirement of only sixteen 8-bit sample points as the window length for calculation and no need for a multiplier render the Sim-DWT easy to implement in an MCU with limited hardware resources. The Sim-DWT is applied in an 8-bit MCU with 6mW power consumption (including IO ports) and was tested for detecting LFP signals in vivo. The design methods and the accuracy of three typical types of mother wavelet functions (Haar, DB4, Morlet) in the Sim-DWT were also tested and compared with those of a PC-based system. The experimental results showed that with appropriately designed cMW functions in the Sim-DWT, HVSs could be detected more accurately than they could be in PC-based software. The present study indicates that the optimized HVS detector (Sim-DWT) can be implemented in an 8-bit MCU with limited hardware resources and is suitable to serve as the digital core in a closed-loop deep brain stimulator microsystem in the future.

Outsourcing data mining tasks is beneficial for data owners who either lack expertise in data mining or sufficient computing resources. However, directly releasing the original data would leak private information. Research on Privacy-Preserving Data Mining (PPDM) is dedicated to addressing this issue, the aim of this research is to reduce the risk of privacy violations and preserve the knowledge in the original data. However, most existing methods in the literature ignore the case in which service providers want to verify the integrity and authenticity of their clients’ data to avoid data tampering before performing data mining tasks. In this paper, a new method is proposed to extend the turtle shell algorithm of data hiding to protect the privacy of the original data and to acquire authentication functions simultaneously. The act of data perturbation is performed by replacing data values with their closest neighbors according to a reference matrix. Further, a message authentication code is hidden in the perturbed data to verify the integrity and authenticity of the perturbed data. The experimental results showed that the proposed method achieved the purpose of data perturbation and outperformed similar methods in satisfying the PPDM requirement.

Remote voting allows the voters to cast their votes remotely at their convenience, and it is desirable in many circumstances. In the literature, a number of verifiable remote voting schemes have been introduced recently. However, some of these schemes are only suitable for the low coercion environment. Since without the receipt‐freeness guarantee, voters can be coerced or bribed to vote the candidate favoured by adversaries. Some other schemes provide a very high level of security assurance, e.g. coercion resistance, but these schemes are normally very complex, and ordinary voters are required to perform complicated crypto calculations. In this study, the authors introduce a generic framework for practical and receipt‐free remote voting. In their proposed scheme, voters do not need to have special knowledge or use any trusted device to cast their votes, and meanwhile, they cannot prove to the others how they have voted.

With the rapid development of Internet technology, images on the Internet are used in various aspects of people’s lives. The security and authorization of images are strongly dependent on image quality. Some potential problems have also emerged, among which the quality assessment and denoising of images are particularly evident. This paper proposes a novel NR-IQA method based on the dual convolutional neural network structure, which combines saliency detection with the human visual system (HSV), used as a weighting function to reflect the important distortion caused by the local area. The model is trained using gray and color features in the HSV space. It is applied to the parameter selection of an image denoising algorithm. The experiment proves that our proposed method can accurately evaluate image quality in the process of denoising. It provides great help in parameter optimization iteration and improves the performance of the algorithm. Through experiments, we obtain both improved image quality and a reasonable result of subject assessment when the cascaded algorithm is applied in image security and authorization.

Data hiding is a technique that allows secret data to be delivered securely by embedding the data into cover digital media. In this paper, we propose a new data hiding algorithm for H.264/advanced video coding (AVC) of video sequences with high embedding capacity. In the proposed scheme, to embed secret data into the quantized discrete cosine transform (QDCT) coefficients of I frames without any intraframe distortion drift, some embeddable coefficient pairs are selected in each block, and they are divided into two different groups, i.e., the embedding group and the averting group. The embedding group is used to carry the secret data, and the averting group is used to prevent distortion drift in the adjacent blocks. The experimental results show that the proposed scheme can avoid intraframe distortion drift and guarantee low distortion of video sequences. In addition, the proposed scheme provides enhanced embedding capacity compared to previous schemes. Moreover, the embedded secret data can be extracted completely without the requirement of the original secret data.

Cloud storage, one of the core services of cloud computing, provides an effective way to solve the problems of storage and management caused by high‐speed data growth. Thus, a growing number of organizations and individuals tend to store their data in the cloud. However, due to the separation of data ownership and management, it is difficult for users to check the integrity of data in the traditional way. Therefore, many researchers focus on developing several protocols, which can remotely check the integrity of data in the cloud. In this paper, we propose a novel public auditing protocol based on the adjacency‐hash table, where dynamic auditing and data updating are more efficient than those of the state of the arts. Moreover, with such an authentication structure, computation and communication costs can be reduced effectively. The security analysis and performance evaluation based on comprehensive experiments demonstrate that our protocol can achieve all the desired properties and outperform the state‐of‐the‐art ones in computing overheads for updating and verification.

In empirical data modelling, a model of system is built up from a set of cases that the system has observed. Eventually, the performance of the inducted model is dominated by the quality and quantity of observations. Feature transformation methods are widely used to improve quality of knowledge ext racted from observations to build up more accurate and robust model. In the paper, a new feature transformation method named dynamical feature bundling for decision tree algorithm is proposed. Dynamical feature bundling groups a set of features in the tree induction phase and it enables decision tree algorithms to 1) make use of features in one bundle together to make collective judgments in splitting phase; 2) learn more reliable and stable knowledge from feature bundles created based on domain knowledge of experts; 3) embed feature transformation step into tree induction phase, and therefore the extra pre-process step which are necessary for static feature transformation methods is inessential. Our experiments show 2%-9% improvements of AUC value on a very imbalanced dataset. Slight improvements are also obtained on a more balanced data set.

Cloud storage is an increasingly popular application of cloud computing, which can provide on-demand outsourcing data services for both organizations and individuals. However, users may not fully trust the cloud service providers (CSPs) in that it is difficult to determine whether the CSPs meet their legal expectations for data security. Therefore, it is critical to develop efficient auditing techniques to strengthen data owners' trust and confidence in cloud storage. In this paper, we present a novel public auditing scheme for secure cloud storage based on dynamic hash table (DHT), which is a new two-dimensional data structure located at a third parity auditor (TPA) to record the data property information for dynamic auditing. Differing from the existing works, the proposed scheme migrates the authorized information from the CSP to the TPA, and thereby significantly reduces the computational cost and communication overhead. Meanwhile, exploiting the structural advantages of the DHT, our scheme can also achieve higher updating efficiency than the state-of-the-art schemes. In addition, we extend our scheme to support privacy preservation by combining the homomorphic authenticator based on the public key with the random masking generated by the TPA, and achieve batch auditing by employing the aggregate BLS signature technique. We formally prove the security of the proposed scheme, and evaluate the auditing performance by detailed experiments and comparisons with the existing ones. The results demonstrate that the proposed scheme can effectively achieve secure auditing for cloud storage, and outperforms the previous schemes in computation complexity, storage costs and communication overhead.

Abstract Online electronic transactions have become more and more popular in recent years. Electronic cash (e‐cash) has acted an important role, but e‐cash is digital data that can be copied easily, resulting in double spending. To ensure the security and fairness of transactions, researchers designed secure e‐cash payment systems. In 1982, Chaum proposed the first payment scheme using an RSA‐based, blind signature to achieve anonymity. Since then, many studies have been conducted, but most related schemes still have high costs associated with the computations. In this paper, we propose a new, secure, e‐cash payment system using elliptic curve cryptosystem (ECC) and secret sharing combined with cloud computing to provide lower cost than previous systems. Our proposed scheme also can meet the requirements of a well‐designed, secure, e‐cash payment system. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Steganography in low bit‐rate speech streams is an important branch of Voice‐over‐Internet Protocol steganography. From the point of preventing cybercrimes, it is significant to design effective steganalysis methods. In this paper, we present a support‐vector‐machine‐based steganalysis of low bit‐rate speech exploiting statistic characteristics of pulse positions. Specifically, we utilize the probability distribution of pulse positions as a long‐time distribution feature, extract Markov transition probabilities of pulse positions according to the short‐time invariance characteristic of speech signals, and employ joint probability matrices to characterize the pulse‐to‐pulse correlation. We evaluate the performance of the proposed method with a large number of G.729a‐encoded speech samples and compare it with the state‐of‐the‐art methods. The experimental results demonstrate that our method significantly outperforms the previous ones on detection accuracy, false positive rate, and false negative rate at any given embedding rates or with any sample lengths. Particularly, this method can successfully detect steganography employing only one or a few of the potential cover bits, which is hard to be effectively detected by the existing methods. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Recently, electronic cash has become a hot topic issue with the concurrent sustainable growth of electronic commerce. In practice, however, most current electronic cash schemes do not satisfy all necessary requirements. In this paper, we point out flaws in the Fanet al.scheme and based on their scheme, propose a new off‐line e‐cash scheme. We use an ElGamal signature instead of the chameleon hash function to revise the problem of forgeability and reusability in the original scheme. We use an expiration date to improve the management efficiency of the bank's database and resolve the lost privacy problem in the recovery protocol. Our proposed scheme provides security without increasing computation and communication costs compared with the Fanet al.scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract This paper proposes a novel secret image sharing scheme, which progressively hides a secret image into multiple different meaningful cover (or host) images by utilizing a magic matrix. The produced shadows are high visual quality, meaningful images that differ from each other. As a result, they are not easy to cause the suspicion by the attackers. Moreover, the secret image in the proposed scheme can be recovered progressively via different numbers of shadows. The more shadows used, the better the quality of the secret image. The experimental results demonstrate the aforementioned advantages of the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract In traditional television (TV) systems, users have to pay for all of the channels provided by the TV service server. However, usually, users are not interested in all of the channels, so they waste money by paying for all of the channels. For this reason, the concept of “pay‐TV” has been developed. A pay‐TV system allows the subscribers to pay for the specific channels they want to watch. If we consider that m users subscribe to n channels in traditional schemes for pay‐TV, the time complexity will be O(mn). It is lower efficiency for user on the pay‐TV system. But, in this paper, we proposed a novel authentication scheme for pay‐TV systems based on Chebyshev chaotic maps, and the time complexity only needs O(m). The purpose of our scheme is focus on efficiency and security. The security and performance analyses showed that our proposed scheme satisfies the essential functionality requirements, withstands potential attacks, and is suitable for real‐world practical implementation. Furthermore, the Burrows–Abadi–Needham logic model was used to prove the correctness of our proposed scheme for the mutual authentication between users and the server. Copyright © 2016 John Wiley & Sons, Ltd.

Iris recognition has gained increasing popularity over the last few decades; however, the stand-off distance in a conventional iris recognition system is too short, which limits its application. In this paper, we propose a novel hardware-software hybrid method to increase the stand-off distance in an iris recognition system. When designing the system hardware, we use an optimized wavefront coding technique to extend the depth of field. To compensate for the blurring of the image caused by wavefront coding, on the software side, the proposed system uses a local patch-based super-resolution method to restore the blurred image to its clear version. The collaborative effect of the new hardware design and software post-processing showed great potential in our experiment. The experimental results showed that such improvement cannot be achieved by using a hardware-or software-only design. The proposed system can increase the capture volume of a conventional iris recognition system by three times and maintain the system's high recognition rate.

In 2014, Turkanovic et al. proposed a smart card-based authentication scheme for heterogeneous ad hoc wireless sensor network. This scheme is very efficient since it employs only hash function and XOR operation. However, we found that Turkanovic et al.'s scheme is vulnerable to impersonation attack with node capture, stolen smart card attack, sensor node spoofing attack, stolen verifier attack, and fails to ensure backward secrecy. We propose an efficient scheme to overcome all those weaknesses. Moreover, we also propose an advanced scheme, which provides perfect forward secrecy without much modification from the first proposed scheme.

Most authentication protocols are used for remote users, in which the registration is achieved through a secure channel and the authentication is achieved through an insecure channel. However, in real life, there are many applications in which the registration is implemented remotely through an insecure channel and the authentication is achieved through a secure channel. In this paper, a novel authentication protocol that is suitable for the insecure registration is proposed. There are three main contributions, i.e. (i) the registration can be implemented through an insecure channel, (ii) the authentication is achieved by image exchange between the registration center and the user and (iii) a proxy user authentication mechanism is proposed, which allows a proxy user to act on behalf of the primary user. The correctness and security of the proposed authentication protocol are proved by both theoretical analysis and experimental simulations.

With the benefits of portability and mobility, laptops have become more and more popular. People usually believe that their personal laptop is safe, and they store sensitive data in it. However, when a laptop is stolen, a malicious thief can obtain illegitimate benefits by extracting sensitive data from the laptop. For this reason, several security mechanisms for protecting sensitive data on laptops have been designed. Kao et al . (2012) designed a protocol (MELP) that allowed laptop users to use their mobile phones as the encryption key to protect their files. Although the mobile‐based mechanism is secure, the authors still believe that it is limited by third‐generation mobile devices and online data encryption/decryption. In this study, the authors present their design about a novel scheme based on secret‐sharing technique. In the authors’ proposed scheme, users can easily encrypt/decrypt their laptop data offline, that is, without access to the Internet. Furthermore, the authors’ security analysis shows that their scheme satisfies all of the essential requirements for security and functionality. Also, the authors’ performance analysis indicates that their proposed scheme has better efficiency, which makes it suitable for use in protecting the data stored on laptops.

The reference‐table‐based (RTB) steganography employs two pixels to conceal a secret digit, according to a predetermined reference table. Most of the RTB steganographic schemes discussed in the literature do not consider the non‐uniformity of the secret digits and the locating numbers indicated by the pixel pairs. In contrast, the non‐uniformity of the locating numbers exists for most of host images. When a non‐uniform secret sequence is embedded, the quality control of the stego image is quite challengeable when compared with a uniform secret sequence. To the best of the authors knowledge, most of the existing algorithms focus on the uniform sequences. In this study, they propose a multi‐round dynamic swap optimisation (MRDSO) algorithm for RTB steganography, which is more suitable for non‐uniform secret sequences. By optimising the permutation of the reference table, the proposed algorithm increases the scenarios that the locating number indicated by the pixel pair is equal to the secret digit, which means that these host pixels can carry the secret digit without any distortion. The experimental results show that the proposed MRDSO algorithm increases the quality of the stego image by 0.1–1.7 dB.

A second‐order steganographic method (SOS) based on pixel pair matching and modification direction exploiting (MDE) is proposed in this study. In SOS, each cover pixel pair is used to conceal two secret digits in a B ‐ary notational system. Therefore the maximum embedding rate (ER) is up to log 2 B bit per pixel (bpp). It is different from the previous MDE‐based methods in which only one secret digit in base B can be embedded into each cover pixel pair and the maximum ER is ½log 2 B bpp. The experimental results demonstrate the improvements to the proposed method in terms of capacity, efficiency and detection rate compared to recent MDE‐based methods. Take B = 3 as an example. The ER is 1.585 bpp and the corresponding average peak‐signal‐to‐noise‐rate is 49.89 dB, demonstrating the best image quality with the same embedding rate compared to recent MDE‐based methods.

Abstract A password‐based authenticated group key exchange protocol assists group participants who possess low‐entropy, human‐memorable passwords in establishing a secure communication channel. In this type of scheme, the server needs to store the users' verifiers in a database. Therefore, it is susceptible to stolen‐verifier attacks. In this paper, we propose a new authenticated group key protocol that eliminates the need of verifier database at the server side. Our protocol is based on a two‐factor authentication that employs both smart card and password. Copyright © 2014 John Wiley & Sons, Ltd.

With the rapid development of the cloud computing, the concepts of cloud computing technology have been applied gradually in many high-tech industries. There are many applications of cloud computing, and among them, cloud storage service is one of the best known because of its extensive utilization in people's daily lives. The use of cloud storage services eliminates the need for people to purchase heavy, expensive devices to provide the large storage space required for their data. Instead, they only need a networked device, which allows them to store their data in the cloud storage server and access the data anywhere. Therefore, more and more people are using the cloud storage server, and this is accompanied by an increasing number of security issues. In this paper, we proposed a secure protocol for the cloud storage service system. Analyses showed that our proposed protocol can withstand potential attacks and that it possesses essential functionalities, such as user anonymity, data dynamics, and data integrity. Therefore, our proposed protocol is suitable for practical implementation. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Embedding secret data in vector quantization (VQ) compressed images with reversibility has been studied extensively in recent years. However, to date, the reported methods have not achieved satisfactory performances of hiding capacity and image compression ratio simultaneously. In this paper, we propose a reversible embedding method based on search‐order coding (SOC) and index parity matching that can hide secret data into the compressed VQ index, that is, SOC index. If the parity of the candidate SOC index matches the current embedding bit and the error caused by SOC encoding is smaller than a pre‐determined threshold, the length of the stego SOC index after embedding is significantly shorter than the original VQ index. On the receiver side, the embedded secret bits can be easily extracted by checking the parity of stego SOC indices, and all original VQ indices can be recovered correctly. Experimental results demonstrate that our method can achieve greater hiding capacity than the recently reported methods for the same image decompression quality. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Reversible data hiding enables the cover image to be restored from the stego image without any loss after the secret message is extracted. In this paper, we proposed a novel reversible data‐hiding scheme for high image quality based on the histogram‐shifting method. In our scheme, we proposed two mechanisms for choosing reference pixels according to the high correlation of adjacent pixels. The differences between reference pixels and their corresponding, neighboring, non‐reference pixels are exploited to hide secret messages according to the difference shifting mechanism. To achieve larger embedding capacity, the reference pixels are designed to further carry secret messages based on the interpolation prediction method. Experimental results showed that our proposed scheme achieved higher embedding payload while maintaining better visual quality of the images than did other schemes. Copyright © 2014 John Wiley & Sons, Ltd.

With the rapid development of network technologies and the continuing growth of the online population, the distribution of streamed media on the Internet has become a cause for concern. In order to securely deliver large-scale message streams, various multicast authentication protocols have been proposed. Recently, Abdellatif et al. proposed a real-time multicast authentication protocol called LAR1. The LAR1 protocol has low computation and communication overheads and uses the erasure code function to deal with the packet loss problem. However, through careful analyses, we find that their scheme cannot resist impersonation attacks on signers and only partially solves the packet loss problem. Moreover, there are still some design weaknesses. Then, we propose a more secure multicast authentication protocol, called IMA, with almost the same computation overheads and lower communication overheads. We adopt the BAN logic to demonstrate the authentication accuracy of our proposed scheme. Comparisons of other related works show that our proposed IMA protocol maintains efficiency and is more secure. Therefore, our proposed protocol is more suitable for practical applications.

ABSTRACT A (t,n) secret sharing scheme (SS) enables a dealer to divide a secret into n shares in such a way that (i) the secret can be recovered successfully with t or more than t shares, and (ii) the secret cannot be recovered with fewer than t shares. A verifiable secret sharing scheme (VSS) has been proposed to allow shareholders to verify that their shares are generated by the dealer consistently without compromising the secrecy of both shares and the secret. So far, there is only one secure Chinese remainder theorem‐based VSS using the RSA assumption. We propose a Chinese remainder theorem‐based VSS scheme without making any computational assumptions, which is a simple extension of Azimuth–Bloom (t,n) SS. Just like the most well‐known Shamir's SS, the proposed VSS is unconditionally secure. We use a linear combination of both the secret and the verification secret to protect the secrecy of both the secret and shares in the verification. In addition, we show that no information is leaked when there are fewer than t shares in the secret reconstruction. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT We propose the notion and define the security model of a certificate‐based threshold signature. The model is a general model that allows both the master secret key and user secret keys to be determined and distributed to the corresponding participators. Furthermore, the model can be easily converted into an identity‐based (ID‐based) threshold signature model to solve the key escrow problem and can be converted into a certificateless threshold signature model. In addition, we propose a secure and efficient certificate‐based threshold signature scheme. Compared with previous ID‐based threshold signature and certificateless threshold signature, our scheme requires no computation of pairings and no trusted dealer. In addition, in our proposed scheme, unlike most schemes that require all members to jointly generate a certificate or a signature, it only requirestor more thantmembers to generate a certificate or a signature. Our proposed scheme can detect dishonest participants as well. Therefore, our scheme is more practical than existing schemes. We show that our scheme is existentially unforgeable against adaptive chosen message attacks under the discrete logarithm assumption. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we propose a novel joint data-hiding and compression scheme for digital images using side match vector quantization (SMVQ) and image inpainting. The two functions of data hiding and image compression can be integrated into one single module seamlessly. On the sender side, except for the blocks in the leftmost and topmost of the image, each of the other residual blocks in raster-scanning order can be embedded with secret data and compressed simultaneously by SMVQ or image inpainting adaptively according to the current embedding bit. Vector quantization is also utilized for some complex blocks to control the visual distortion and error diffusion caused by the progressive compression. After segmenting the image compressed codes into a series of sections by the indicator bits, the receiver can achieve the extraction of secret bits and image decompression successfully according to the index values in the segmented sections. Experimental results demonstrate the effectiveness of the proposed scheme.

ABSTRACT Digital rights management (DRM) is a term for access control technologies that are used by hardware manufacturers, publishers, copyright holders, and individuals to limit the use of digital content and devices. Enterprise digital rights management (E‐DRM) is the application of DRM technology to prevent illegal users from accessing the confidential data of an enterprise. In 2010, Chang et al. proposed an efficient E‐DRM scheme to solve the flaws of Chen's scheme. However, we still found some weaknesses in their scheme. In this article, we propose an improved secure and efficient E‐DRM mechanism based on a one‐way hash function and exclusive‐or. Our mechanism overcomes the weaknesses in the scheme of Chang et al. and also reduces computation costs. In addition, we used BAN logic to show the correctness of our mechanism. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Radio frequency identification (RFID) systems rely on the significant technology of remote, automatic identification with small, low‐cost RF elements to complete RF communications securely among all entities. This technology must ensure that secret information or data communicated via radio transmission are not vulnerable to intentional interception, modification for tracing the individual tag holder, or the attachment of any item. With the increase in the number of applications of RFID systems, security and privacy problems have become the focus of significant concern. In this paper, we propose an authenticated RFID security mechanism based on chaotic maps that guarantees a mutually authenticated process and uses a simple and robust chaotic cryptosystem to implement a practical RFID mechanism. The security of the transmitted information and the privacy of personal information or data were our main concerns in the proposed scheme. Also, we have provided detailed information concerning authentication proof and security aspects of our new design. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we propose a novel prediction-based reversible steganographic scheme based on image inpainting. First, reference pixels are chosen adaptively according to the distribution characteristics of the image content. Then, the image inpainting technique based on partial differential equations is introduced to generate a prediction image that has similar structural and geometric information as the cover image. Finally, by using the two selected groups of peak points and zero points, the histogram of the prediction error is shifted to embed the secret bits reversibly. Since the same reference pixels can be exploited in the extraction procedure, the embedded secret bits can be extracted from the stego image correctly, and the cover image can be restored losslessly. Through the use of the adaptive strategy for choosing reference pixels and the inpainting predictor, the prediction accuracy is high, and more embeddable pixels are acquired. Thus, the proposed scheme provides a greater embedding rate and better visual quality compared with recently reported methods.

With the advances of network technology, electronic auctions have become increasingly popular in recent years. In 2012, Xiong proposed a bidder-anonymous auction scheme based on revocable ring signature to enhance the computation efficiency and provide a dispute protocol. Nevertheless, we find that it has design weaknesses and is vulnerable to denial-of-service attacks. Furthermore, in an actual electronic auction system such as Yahoo! and eBay, a user can play the role of not only a bidder, but also an auctioneer. Considering the essentiality of the discussion of the secure products on-shelf process for auctioneers, we design a new electronic auction system with a secure on-shelf mechanism in this paper. In our system, users can freely participate in or host an auction if and only if they have registered at the agent center. Analyses demonstrate that our auction system not only resolves the problems in Xiong 's scheme, but also provides more features with higher security.

Reversible/lossless image data hiding schemes provide the capability to embed secret information into a cover image where the original carrier can be totally restored after extracting the secret information. This work presents a high performance reversible image data hiding scheme especially in stego image quality control using image inpainting, an efficient image processing skill. Embeddable pixels chosen from a cover image are initialized to a fixed value as preprocessing for inpainting. Subsequently, these initialized pixels are repaired using inpainting technique based on partial differential equations (PDE). These inpainted pixels can be used to carry secret bits and generate a stego image. Experimental results show that the proposed scheme produces low distortion stego images and it also provides satisfactory hiding capacity.

Although the objective of secure communication can be achieved by using cryptographic tools, the undeniability that results from cryptographic properties may create a potential threat to the sender of the message. Unfortunately, most existing deniable protocols only provide 1-out-of-2 deniability. When both parties (the sender and the receiver) are allowed to deny generating the message, a dispute might occur between these two parties. The 1-out-of-2 deniable protocol can result in an unfair resolution of the dispute. Therefore, we propose a new model of deniability, called 1-out-of-∞ deniability, that can provide full deniability. The 1-out-of-∞ deniability protocol allows the originator of the message to deny that he or she generated the message, since there are an infinite number of possible message generators; at the same time, all transmitted messages can be protected and authenticated between the sender and the intended receiver. Our design can be implemented by using any public-key cryptography technique. We also analyze the correctness of the proposed protocols based on logical rules, and two practical examples are given to illustrate our design.

Reversible steganography becomes a popular hiding problem in recent years. A reversible steganographicmethod can reconstruct an original image without loss from the stego-image after extracting the embedded data. Unlike traditional reversible methods in which data is hidden in uncompressed images, we propose a reversible scheme for BTC (block truncation coding)-compressed images. The secret data embedded in the compressed image are more difficult to detect than in the uncompressed image. To achieve reversibility, the properties of side matching and BTC-compressed code are applied. The experimental results show that the proposed method is feasible for BTC-compressed images and can embed one more bit in each BTC-encoded block.

The secret-sharing mechanism has been widely applied to the e-commerce, communications and multimedia fields. With sufficient shares, the involved participants can cooperate to reveal secret data. Fraudulent participants, however, may provide a fake shadow in order to fool others. Consequently, cheating prevention has become a critical essential for secret sharing systems. In this article, the authors propose an efficient image secret sharing scheme that can resist cheating attacks. The simulator shows that the novel scheme is sensitive to cheating detection and cheater identification. In particular, the new method allows an authorised participant to reveal a lossless secret image and to further restore the valued host image without distortion. The reversibility of the secret sharing system provides practicability and widespread potential for preserving medical images, military images and artistic images.

In this paper, we propose an efficient secret sharing scheme without pixel expansion. The scheme first uses the VQ-compression method to compress a secret image. This allows senders to share a larger secret image than other methods. Moreover, the proposed method also allows participants to reconstruct a lossless secret image. The generated shadows are meaningful with high quality, so the image does not attract any suspicion from attackers. Because the scheme uses XOR operation during the construction and revealing phases, it is suitable for secret sharing applications.

Fragile watermarking is applied to protect the content integrity of digital images. The main concerns related to watermarking include retaining the quality of the watermarked image and retaining the ability to detect whether any manipulation has occurred. Because recent watermarking techniques seriously distort the quality of the protected image after embedding the authentication code into the image content, attention has been drawn to how to satisfy both the need for image fidelity and detection ability. To account for the influence from both essentials, a novel algorithm is proposed in this article. The new scheme utilizes a weighted-sum function to embed ( n + 1) authentication bits into a block with 2 n pixels by modifying only one original pixel with (±1). With fewer authentication codes, the new process can protect the content of the image. The experimental results demonstrate that the approach can guarantee the fidelity of the watermarked image while retaining tamper-proof functionality.

Increasing numbers of mobile users are being allowed to use wireless networks, and universal access is being promoted. In the absent of a single, trusted authentication server, it is a great challenge to ensure the inter-domain security, which makes it feasible for users to migrate into foreign domains. Thus, an authentication mechanism is needed between mobile users and foreign servers, and an authenticated key also is highly desirable to support secure communications in wireless networks. In addition, maintaining the anonymity of users is an important security requirement, such as the information about customers's behaviors. Recent research has focused on these issues and has provided definitions and some constructions. Importantly, Tang and Wu proposed an efficient mobile authentication scheme, which they called `'Efficient Mobilization Authentication Scheme" (EMAS), which enjoys both computational efficiency and communication efficiency that exceed the efficiencies of other recent mobile authentication schemes. Unfortunately, we found out that Tang and Wu's scheme does not meet the basic security requirements. Therefore, to develop a more acceptable mobile authentication scheme, we propose a self-verified mobile authentication scheme that has a novel architecture. To provide the better computation efficiency and storage efficiency, our scheme does not require of long-term secret keys on the servers.

In this paper, we proposed a novel (2, 2) secret sharing scheme applied for grayscale images. This scheme can produce two noise-like shared images that help avoid a secret image being grabbed. The construction phase of shared image consists of producing two shared images of the secret image by a (7, 4) Hamming code operation. To prevent attackers from forging shared images, a logo is embedded into the shared images first by using Tianpsilas reversible data embedding scheme. Later, the embedded secret image is shuffled by Torus automorphism to enhance its security. Experimental results show that the noise-like shared images can be successfully generated by our proposed scheme.

A new watermark method for detecting image tampering is proposed in this paper. First, an authentication number is created by using a pair of watermark pixels as the coefficients of a linear function. The central pixel of a host block as the input data to the linear function. The authentication information of that block is then embedded into the host image using the LSB matching revisited embedding procedure. In the detection stage, ISB matching revisited will be applied to extract the authentication information and to verify whether the image blocks have been tampered with. The authentication information establishes the block feature correlation. This kind of correlation helps resist VQ attack.

In this paper, a good data hiding scheme has been proposed. This scheme embeds messages into a still image based on (Fridrich, et.al; 2005; Dumitrescu, et.al; 2003) Hamming codes oriented wet paper codes . It embeds a segment of three secret bits into a group of seven cover pixels at a time. The experimental results show that the proposed scheme achieves high embedding efficiency and acceptable capacity.

Protecting data transmitted over the Internet has become a critical issue driven by the progress in data digitalization and communications networking over the past decade. The content being transmitted can be in the form of images, text and voice. To ensure that transmitted data are secure and cannot be tampered with or noticed by malicious attackers, several approaches have been proposed. Steganography is one general approach among them. The hiding capacity and image quality of stego-images are two major measures with which to evaluate an image hiding scheme. To enhance the hiding capacity of Fridrich et al.'s scheme, an improved image hiding scheme for grayscale images based on wet paper coding is proposed in this paper. The significant difference between Fridrich et al.'s scheme and ours is that we shuffle all the pixels in the host image by adopting toral automorphism, then segment all pixels before using our proposed wet pixel hiding strategies to hide the data. Experimental results show that our proposed scheme embeds a larger-sized secret image while maintaining acceptable image quality of the stego-image better than Fridrich et al.'s scheme does. Moreover, the computational cost of our proposed scheme is significantly less than that of Fridrich et al.'s scheme because inverse matrix and multiplication operations are not required. Therefore, our proposed scheme is suitable for real time applications.

Image authentication is an important research topic of maintaining the integrity of digital image contents. Fragile image authentication is the technique for achieving the goal of image content integrity maintenance. This article presents a fragile image authentication scheme based on the concept of wet paper codes. The proposed scheme modifies dry pixels on an image to conceal an image signature. The proposed authentication scheme can exactly detect the tampered area on a tampered image. For saving computation cost of signature embedding, an exclusive-or operation is used in the proposed authentication scheme. The experimental results show that the proposedmethod not only has good visual quality of an authorized image but also successfully detects tampered areas on a tampered image.

Visual Cryptography (VC) has drawn much attention for providing the service of secret communication. Basically, VC is the process of encoding a secret into several meaningless shares and later decoding the secret by superimposing all or some of the shares without any computation involved. VC has been adopted to support some practical applications, such as image authentication, visual authentication, image hiding, and digital watermarking. Unfortunately, in many applications, VC has been shown to suffer from the "cheating problem" in which the disclosed secret image may be altered by malicious insiders who are called "cheaters." While ubiquitous computing has been well developed, it has recently occurred to people in both academia and industry that research could benefit more from computational VC by introducing light-weight computation costs in the decoding phase. In this paper, a simple scheme is proposed to conquer the cheating problem by facilitating the capability of share authentication. It is worthwhile to note that the proposed scheme can identify for certain whether cheating attacks have occurred or not, while other schemes that have the same objective frequently provide a vague answer. In addition, the proposed scheme effectively addresses the two main problems of VC, i.e., the inconvenience of meaningless share management and the challenge of achieving difficult alignment.

The advances of network technologies and digital devices facilitate users to exchange multimedia data over the public networks. However, this also raises significant concerns about how to protect sensitive multimedia data from being illegally copied and unauthorized modifications. Thus, this paper proposes a fragile watermarking method to detect illegitimate alterations of the watermarked data. The proposed method embeds a grayscale watermark image into a grayscale cover image in a block-by-block manner by using discrete Fourier transform. Experimental results show that the proposedmethod can successfully and exactly detect and localize any tampered regions of the watermarked image.

Information hiding is a technique that embeds secret data in digital media for using in a variety of applications, including ownership protection, authentication, access control, annotation and so on. In this paper, we propose an information hiding scheme based on quantization-based embedding technique to conceal information in gray-scale image. The proposed scheme was tested with a variety of gray images. According to the experimental results, hidden information can be extracted correctly and quickly from the stego image. In addition, the stego image has only a little distortion compared with the cover image. The proposed scheme can not only hide a large amount of information in the cover image, but can also repair the stego image such that the repaired image is almost the same as the cover image.

In this paper, watermarking authentication schemes for binary images are investigated. Dual-Pair Blocks (DPBs), 2 × 2 blocks with two black and two white pixels, are employed as the basic units of information embedding. With 6 distinctive patterns obtained by pixel patterns, a DPB can be represented by a senary value as the embedding information. Variable length coding is adopted to enhance the information embedding capacity of the DPB. The secret key of the watermarking is a pseudo random function with a seed value, which selects DPBs for information embedding. Based on the aforementioned framework, two novel watermarking schemes are presented. The first one, named dual-pair block authentication (DPBA), is an irreversible one featuring high information embedding capacity, and well visual quality. The second scheme, named senary Huffman compression authentication (SHCA) scheme, supports reversible watermarking unique in the literature. Matching-pair technique is further employed both schemes to reduce the number of altered bits after watermarking for better visual quality. Experimental results show that, the DPBA scheme has the highest information embedding capacity on all comparing schemes. The average compression ratio of the reversible SHCA scheme is up to 38.96%. The average distortion rates per accessed DPB for the proposed DPBA and SHCA schemes are 0.485 and 0.46, respectively.

Unlike gambling, lottery games can exist in a lawful form to raise funds for charitable institutions. Owing to the expeditious development of network technology, lotteries over the Internet have become an inevitable trend. Since the Internet allows people to communicate with each other without direct contact, it is more difficult to guarantee the security and fairness of online lotteries than for conventional lottery games. However, electronic lottery methods can also provide something that conventional lottery mechanisms cannot: they allow players to purchase tickets at any time and in any place where they can access the Internet. The authors propose an online lottery mechanism that can confirm the propositions of general lottery games. Specifically, this novel method not only allows players to make t-out-of-n numbers in lotteries without iterative selection but also preserves the privacy of players' choices, making the system more similar to traditional lottery games.

Fault-based cryptanalysis has been developed to effectively break both private-key and public-key cryptosystems, making robust finite field multiplication a very important research topic in recent years. However, no robust normal basis multiplier has been proposed in the literature. Therefore, this investigation presents a semisystolic Gaussian normal basis multiplier. Based on the proposed Gaussian normal basis multiplier, both concurrent error detection and correction capabilities can be easily achieved using time redundancy technology with no hardware modification.

Content digitization transforms a source into its digital form through digital cameras or scanners. This transformation often leads to the irregular distribution of image illumination, which may distort the quality of digital images. Engineers have provided many mechanisms to solve the degradation problem; however, these works mainly focus on dealing with full text images. In this letter, we propose a novel method to balance the illumination distribution of the transformed image. Experimental results show that the new method is capable of improving the quality of degraded text images. Particularly, unlike related works, the new method can effectively adjust the illumination distribution of a text-photo image to enhance the image quality.

This paper presents a novel dual watermarking mechanism for digital media that embeds a recognizable pattern into the spatial domain and an invisible logo into the frequency domain. Undoubtedly, visible watermarking is important for protecting online resources from unauthorized reproduction. Due to the visibility of embedded patterns, however, watermarked digital media are vulnerable to the inpainting attack and common signal processing operations. Utilizing hybrid strategies, simulation results show that the novel method can resist these attacks. In particular, the new mechanism allows legal subscribers to restore an unmarked image, whereas other dual watermarking schemes do not. This feature makes it suitable for protecting artistic and valuable media.

In this letter, we present a reversible data hiding scheme based on histogram modification. We exploit a binary tree structure to solve the problem of communicating pairs of peak points. Distribution of pixel differences is used to achieve large hiding capacity while keeping the distortion low. We also adopt a histogram shifting technique to prevent overflow and underflow. Performance comparisons with other existing schemes are provided to demonstrate the superiority of the proposed scheme.

In this paper, we propose a novel scheme called a self-verifying visual secret sharing scheme, which can be applied to both grayscale and color images. This scheme uses two halftone images. The first, considered to be the host image, is created by directly applying a halftoning technique to the original secret image. The other, regarded as the logo, is generated from the host image by exploiting the interpolation and error diffusion techniques. Because the set of shadows and the reconstructed secret image are generated by simple Boolean operations, no computational complexity and no pixel expansion occur in our scheme. Experimental results confirm that each shadow generated by our scheme is a noise-like image and eight times smaller than the secret image. Moreover, the peak signal-to-noise ratio value of the reconstructed secret image is larger than 33 dB. Based on the extracted halftone logo, the proposed scheme provides an effective solution for verifying the reliability of the set of collected shadows as well as the reconstructed secret image. Furthermore, the reconstructed secret image can be established completely if and only ifkout ofnvalid shadows have been collected. To achieve our objectives, four techniques were adopted: error diffusion, image clustering, interpolation, and inverse halftoning-based edge detection.

In this paper, a novel data hiding method employing reduplicating embedding is proposed. This method uses seven most significant bits to embed two secret digits and sets the least significant bit as an indicator. Therefore, two secret digits can be embedded into one pixel-group without using the original cover-image and extra data. Because of repeating to embed secret data, our method can provide higher embedding capacity than previous methods, and meanwhile keep a satisfactory image quality and ensure security.

The object of our research is to embed secret images with high-capacity and high-quality of the stego image. Taking advantage of the small difference among close neighboring pixels, the secret image can be compressed to be a collection of which each component is either a secret pixel or the difference between two neighboring pixels. Then the collection can be entirely embedded into cover image by the improved EMD embedding scheme. The experimental results show that the average bit rate is up to 2.723 bpp and the average PSNR is about 36.4 dB.

Lossless data hiding enables the embedding of messages in a host image without any loss of content. In this paper, we present a lossless data hiding technique based on histogram modification for image authentication that is lossless in the sense that if the marked image is deemed authentic, the embedding distortion can be completely removed from the marked image after the embedded message has been extracted. This technique uses characteristics of the pixel difference to embed more data than other histogram-based lossless data hiding algorithms. We also present a histogram shifting technique to prevent overflow and underflow problems. Performance comparisons with other existing lossless data hiding schemes are provided to demonstrate the superiority of the proposed scheme.

Reversible data embedding is a very important issue, especially in dealing with sensitive images such as military data and medical data. Based on the edge-directed prediction scheme, the authors propose a reversible data-embedding scheme to embed secret information in original images. In the proposed scheme, an embedded pixel value is generalised according to a predetermined threshold and the difference between the predicted pixel value and its original pixel value. Experimental results show that the proposed scheme can provide great payload capacity while preserving the quality of the stego-image. The hiding capacity of the proposed scheme is about 1.7 times that of Tian's data-embedding scheme and twice that of Celik's.

Abstract Since the early 1990s, engineers have proposed many mechanisms for providing confidential communications and data integrity in mobilead hocnetworks (MANET). The deficiency of fastened infrastructure makes mobilead hocnetworks more vulnerable to security threats than conventional wireless networks. MANET can be considered as a short‐lived collection of mobile nodes that communicate with each other in between. All involved nodes in the network system share the responsibility for routing, accessing, and communicating. The main challenges on the design of MANET include high power saving and low bandwidth. The public key infrastructure (PKI) is a well‐known mechanism used to provide data integrity and confidential communications in MANET recently. The computation loads of PKI, however, are heavy to each mobile node in the cluster. Without adopting PKI cryptosystems to en/decrypt messages, we propose a novel version based on a variant Diffie‐‐Hellman protocol. The novel method can preserve the forward and backward secrecy of group communications. Specifically, the new method outperforms related works in the computation overheads of involved nodes while communicating with other nodes. Copyright © 2008 John Wiley & Sons, Ltd.

Vector quantization (VQ) is a simple and widely used compression technology in many applications. For image compression, VQ provides both a fixed compression ratio and maintains acceptable distortion. However, the performance of VQ still can be improved in terms of the image quality of compressed images and codebook size used for encoding and decoding. In this paper, a new VQ-like image compression method is proposed to improve the performance of traditional VQ by using locally adaptive coding concept. The experimental results confirm that the image quality of the compressed image offered by the proposed method is higher than 30 dB on average, and the number of codewords used in our codebook is less than that required by traditional VQ.

Hwang, Lu and Lin (2003) proposed a (t, n) threshold proxy signature scheme, based on the RSA cryptosystem. Later, Wang et al. (2004a) indicated that this scheme was insecure because the original signer's private key could be derived. Moreover, the delegates' identities had to be chosen carefully. In this paper, we propose an improved RSA-based (t, n) threshold proxy signature scheme, which withstands this security flaw and also offers the convenience of choosing identities at will.

The purpose of data hiding with reversibility property is to recover the original cover media after extracting the hidden data from the stegomedia. In this paper, we propose a reversible data-hiding scheme for embedding secret data in VQ-compressed codes based on the declustering strategy and the similarity property of adjacent areas in a natural image. Two declustering methods are proposed using the minimum-spanning-tree and the short-spanning-path algorithms, respectively. The proposed data-hiding method can achieve the benefits including easy implementation, completely recovering the original compressed codes, and high efficiency of embedding and extraction processes. The experimental results also show that the proposed method has more flexible and higher embedding capacity than other schemes.

This paper presents a fast codebook search method for improving the quantization complexity of full-search vector quantization (VQ). The proposed method is built on the planar Voronoi diagram to label a ripple search domain. Then, the appropriate codeword can easily be found just by searching the local region instead of global exploration. In order to take a step further and obtain the close result full-search VQ would, we equip the proposed method with a duplication mechanism that helps to bring down the possible quantizing distortion to its lowest level. According to the experimental results, the proposed method is indeed capable of providing better outcome at a faster quantization speed than the existing partial-search methods. Moreover, the proposed method only requires a little extra storage for duplication.

In 2003, Chang and Chang proposed three efficient anonymous auction protocols. Later, Jiang et al., claimed that the initiation of their protocols would result in a security drawback and also presented an improvement. After taking this security drawback and computation efficiency into consideration, we propose the efficient anonymous auction protocols in this article.

Sun proposed an efficient remote authentication scheme using smart cards which ensures low communication and computation cost, but the user cannot choose his/her password at will. Thus, Wu and Chieu presented an improvement. However, Yang and Wang pointed out that Wu and Chieu's scheme is vulnerable to the password guessing and forgery attacks. To preserve the advantage of Sun's scheme and Wu and Chieu's scheme and to amend the security weakness, we propose an improvement in this paper. Moreover, our scheme needs no time concurrency mechanism and ensures mutual authentication.

The LSB-based data hiding scheme is to embed the secret data into the least significant bits of the pixel values in a cover image. When the size of the embedded secret data is bigger, this processing will degrade the quality of the stego-image so significantly as to catch the attention of hostile interceptors. To overcome this drawback, in this paper, we propose a high-capacity, high-quality data hiding scheme. First, the proposed scheme divides the pixel value range into several non-overlapping regions. For each spatial pixel value in the cover image, we embed one secret-bit into it by using the difference expansion technique in single pass, where the difference is calculated between the spatial pixel and the start or end of the region it is situated in. Owing to the use of the difference expansion technique, this processing is also suitable for multiple-layer embedding. Experimental results show significant improvement with respect to the embedding capacity. In addition, the distortion in the stego-image is almost invariable regardless of the increase of the load of embedded data.

Many researchers have studied reversible data hiding techniques in recent years and most have proposed reversible data hiding schemes that guarantee only that the original cover image can be reconstructed completely. Once the secret data are embedded in the compression domain and the receiver wants to store the cover image in a compression mode to save storage space, the receiver must extract the secret data, reconstruct the cover image, and compress the cover image again to generate compression codes. In this paper, we present a reversible data hiding scheme based on side match vector quantization (SMVQ) for digitally compressed images. With this scheme, the receiver only performs two steps to achieve the same goal: extract the secret data and reconstruct the original SMVQ compression codes. In terms of the size of the secret data, the visual quality, and the compression rate, experimental results show that the performance of our proposed scheme is better than those of other information hiding schemes for VQ-based and SMVQ-based compressed images. The experimental results further confirm the effectiveness and reversibility of the proposed scheme

The reversible steganographic method allows an original image to be completely reconstructed from the stegoimage after the extraction of the embedded data. The traditional reversible embedding schemes are not suitable for images compressed using vector quantization (VQ) and usually require the use of the location map for reversibility. In this paper, we propose a reversible embedding scheme for VQ-compressed images that is based on side matching and relocation. The new method achieves reversibility without using the location map. The experimental results show that the proposed method is practical for VQ-compressed images and provides high image quality and embedding capacity

Abstract A mobile agent is a technological promising product. It is an executive program that can migrate from host to host, performing tasks autonomously. However, in an open environment, the mobile agent has many potential threats. Security becomes a critical issue for the mobile agent. Recently, Volker and Mehradad have constructed a mobile agent structure and designed a key assignment strategy to prevent the sensitive data to from being accessed by an unauthorized host. However, their scheme requires a larger agent size and a higher computational cost. In this paper, we shall propose an efficient key assignment scheme to enhance the performance of Volker and Mehradad's scheme. Only few computations and a fixed and small storage are required in our proposed scheme. Furthermore, our scheme can achieve the dynamic key management efficiently. It can also be implemented simply and practically. Copyright © 2006 John Wiley & Sons, Ltd.

The medical images have an important characteristic that the pixel-to-pixel correlation is very close. In order to preserve this characteristic, we propose a lossless medical image compression method in this paper. Our method builds the modified S-tree structure to make each block contain similar pixels. Therefore, our method can reduce the needed number of bits to record those pixels. The experimental results also show that our method is better than other methods in almost all cases.

In this paper, we propose an approach for hiding a secret image in a cover image. In the beginning, the fractal image compression method is used to compress the secret image, and then we encrypt this compressed data by DES. Finally, we embed the encrypted data into the middle-frequency domain of DCT. After embedding the secret image, the goal of steganography can be successfully achieved.

Due to the rapid development of science and techniques, people can remotely access computers over the networks. Thus, user authentication and key agreement become more and more important to ensure the legality of the user and the security of later communications, respectively. Because the number of servers providing the facilities for the user is usually more than one, the concept of multi-server protocols is introduced. On the Internet, each server usually provides various services, and each service provided by the server may not be accessed by the user. Hence, access control is required in the multi-service environment. In 2004, Juang proposed a multi-server authentication scheme with key agreement. However, access control is not taken into account in Juang's proposed scheme, so we propose an efficient multi-server password authenticated key agreement scheme with access control in this article.

Indices are modified so that the secret data can be hidden into the index-based cover image, and thereby the problem of the stego-image quality degradation occurs. If the stego-image quality degradation problem can be solved enabling the receiver to reconstruct the original indices after extracting the hidden secret data from the index-based stego-image, then the compressed cover image can be used repeatedly by different users. To achieve our goal, in this paper, a reversible data-hiding scheme based on a modified side match vector quantization (SMVQ) technique is proposed. Our experimental results confirm the effectiveness and the reversibility of the proposed scheme.

A general approach for constructing t-out-n oblivious transfer is more practical than 1-out-n oblivious transfer for applications. To guarantee the quality of growing popular communication service, an efficient t-out-n oblivious transfer scheme is urgently desired. Based on the RSA and two-lock cryptosystem, we propose an efficient t-out-n oblivious transfer scheme in this paper. Compared with existing oblivious transfer schemes, our scheme can reduce many computations and communications for both the sender and the receiver. Moreover, our scheme is very suitable for smart-card users.

Based on the factorization problem, a modified (t, n) threshold group-oriented scheme with message recovery is proposed. In the proposed scheme, any combination of more than t members of the group can cooperate to generate the modified group secret key. By using the proposed scheme, the transmission of the signature is secure. Besides, the message linkage blocks which are removed by an eavesdropper can be detected.

The forward secure property is important in digital signature schemes. Many schemes have been proposed for the forward secure signature. Among them, only a few have been proposed for the forward secure blind signature. The blind signature is, however, a critical technique in e-business and other applications and, thus, more research should be devoted on it. This paper focuses on the design of forward secure blind signature schemes. Digital signature schemes have been proposed and discussed for years. Some of them are efficient and secure. Even specific computing hardware is designed for these schemes. Our idea is, therefore, to combine two existing schemes, Koyama's master key scheme and Chaum's blind signature scheme, so that a forward secure blind signature scheme results. This proposed scheme is also fully examined for the forward secure property and the blind signing property. Since these two schemes are both based on the RSA cryptosystem, the security of the proposed scheme depends on the difficulty of solving the factoring problem.

Most of existing user authentication schemes require high computation cost of exponentiation operations for the purpose of security. However, it is not suitable for mobile devices. In this article, we propose a low computation cost user authentication scheme for mobile communication. Our scheme uses only one-way hash functions and smart cards and can be implemented efficiently. The proposed scheme not only resolves the weakness appeared in existing methods but also suits for mobile communication.

People only can remember simple or meaningful passwords. In three-party key exchange protocols with password authentication, a client is allowed to share an easy-to-remember password with a trusted server so that two users can negotiate a session key for secure communication. Steiner et al. proposed a three-party protocol based on the encrypted key exchange protocols in 1995; but, their protocol suffered from off-line and undetectable on-line password guessing attacks. In 2000, Lin et al. proposed a secure three-party protocol with server's public key. Since a certificate is needed, this protocol is not practical for some environments. In 2001, Lin et al. proposed a new three-party protocol without server's public key with two more rounds. Later, Lin et al. proposed an improvement. We propose a secure three-party EKE protocol with only five rounds in this paper.

Recently, an improved digital signature scheme was proposed by He. However, there is a weakness in He's scheme, where the forgers can forge valid signatures. In this paper, we show that there is also a drawback that a public key may have more than one corresponding secret key, where the verifier cannot determine who the real signer is. As a result, we improve He's scheme to overcoming the above flaws. Moreover, we propose a message-passing protocol. In the proposed protocol, the delivered message can be extended by the valid senders, and the appended signatures are used to ensure the integrity of the message and to verify each signer of each sub-message.

Most computers are linked together such that facilities can be shared through the connection of networks. However, resources provided by remote servers over the Internet are not free for all users. If someone wants to access facilities from certain remote server, he/she has to pay for the access request. On the other hand, providers of the facilities have to make their resources accessible under appropriate protection. The combination of password authentication scheme and key agreement mechanism is a practical solution for validating remote users and providing secure communications between a remote system and login users. Without loss of generality, users who want to access a remote server must pass several examinations in the process of authentication. Only after users are authorized by the remote system, they can have the access right to resources provided by the remote server and share a session key with the remote system to ensure the following communications; otherwise, the remote system can reject their access requests. Researchers have proposed lots of password authentication schemes to improve the authentication efficiency and security, so that password authentication has become a popular research topic recently. Employing a time-bounded mechanism, this article describes a novel version which not only achieves the requirements of general password authentication schemes but also allows the remote system to effectively conduct the valid access phase of each legitimate user.

The aim of deniable authenticated protocol allows the receiver to identity the source of a given message but not prove the identity of the sender to a third party. In this article, we propose a new non-interactive deniable authenticated protocol based on Diffie-Hellman key distribution protocol. It has lower communication costs and computational complexity than previously proposed schemes.