Search Results (193)

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article

Organizations have been moving on-premises data functionalities to cloud storage environments. The need for advanced hybrid cryptography is deemed a promising solution for securing data on cloud storage. This scoping review explores the application of hybrid cryptographic systems for data hiding in cloud storage. It focuses on identifying global research trends, technological approaches, and contextual gaps in implementation. The review systematically examines the literature from major scholarly databases to identify existing models that combine traditional and modern cryptographic techniques to enhance data confidentiality, integrity, and authenticity against cloud-based security threats. Out of the 8250 eligible papers, 24 were included in the review. The findings reveal that the majority of scholarly contributions originate from Asia, averaging 87.5%, as reflected in the distribution of included articles by continent. Particularly, India and China dominate in the space, with a complete absence of studies from Africa, including South Africa. This geographical disparity underscores a significant research gap in the contextualization of hybrid cryptographic frameworks suited to Africa’s unique infrastructural and regulatory environments. The review further reveals a limited focus on the development of lightweight, scalable, and adaptable hybrid cryptographic schemes. Such approaches are essential for addressing challenges related to bandwidth limitations, computational efficiency, and regulatory compliance in developing regions. Consequently, this study contributes by establishing a comprehensive knowledge map of hybrid cryptography for cloud security, emphasizing the necessity for region-specific, context-aware frameworks. The findings provide a foundation for future investigations aimed at developing robust efficient hybrid cryptographic models that can strengthen data security in African cloud infrastructures. Full article

In intensive commercial pig production systems, the spatial distribution of elimination and lying behaviors plays a crucial role in pen hygiene, management efficiency, and animal welfare. Pen partition design and the provision of hiding facilities are key structural factors that may influence pigs’ spatial preferences; however, systematic evaluations of their combined effects remain limited. A total of 108 growing–finishing pigs were used in a 3 × 2 factorial design to assess the effects of different partition types and hiding facility configurations, as well as their interaction, on the spatial distribution of elimination and lying behaviors. Behavioral data were analyzed using non-parametric statistical methods. The results showed that partition type and hiding facilities significantly influenced the spatial patterns of elimination and lying behaviors (p < 0.05), whereas no significant effects were observed on total daily elimination duration, elimination frequency, or lying posture distribution (p > 0.05). Elimination behavior was predominantly concentrated in the slatted floor area. The combination of a front-closed partition with a hiding facility significantly increased the proportion of disturbed elimination events (16.2 ± 14.3%), which was higher than that observed in the open partition combined with a hiding facility (7.9 ± 7.6%, p < 0.05). In contrast, the rear-closed partition design was associated with atypical elimination occurring on the solid floor area. Overall, pigs showed a clear preference for lying on the solid floor. The front-closed partition combined with a hiding facility significantly reduced the proportion of lying on the solid floor (64.6 ± 8.5%), whereas the open partition combined with a hiding facility resulted in a higher-than-average proportion of solid-floor lying behavior (80.6 ± 8.9%). These findings indicate that an open partition design combined with a hiding facility is more effective in maintaining functional separation between elimination and resting areas while reducing disturbed elimination events. This study provides experimental evidence to support structural optimization of growing–finishing pig housing, contributing to improved pen hygiene and enhanced animal welfare. Full article

The rapid growth of digital communication has heightened the need for the secure transfer of sensitive image data. This is due to the increasing threats posed by cyberattacks and unauthorized access. Traditional encryption methods, while effective for text and binary data, often face significant challenges when applied to images, due to their larger size and complex structure. These characteristics make it difficult to provide a robust security solution. In this paper, we present a fast and efficient hybrid image encryption and steganography algorithm that leverages a substitution–permutation network (SPN), a chaotic logistic map (CLM), and least-significant-bit (LSB) substitution. This approach aims to improve data security and confidentiality while maintaining low computational complexity. The chaotic map generates random sequences for substitution and permutation, ensuring high unpredictability. The SPN framework improves the confusion and diffusion properties of the encryption process. The LSB substitution method hides the encrypted data values within the pixels of the cover image. We evaluate the security and efficiency of the proposed algorithm using various statistical tests, including measurement of the mean square error (MSE) and peak signal-to-noise ratio (PSNR) and pixel difference histogram (PDH) analysis. The results indicate that our algorithm outperforms many existing methods in terms of speed and efficiency, making it suitable for real-time hybrid encryption and steganography applications. Full article

We developed a novel pattern-based dual-image reversible data hiding scheme utilizing vertical bars and bitten square blocks to improve data capacity, security, embedding efficiency, and visual quality. By embedding secret messages into two grayscale images that closely match the original, the approach significantly lowers the risk of detection by unauthorized parties. The technique includes reference matrix construction, position-based embedding, and perfect message extraction and image recovery. Experimental results demonstrate that the pattern-based method achieves a superior balance between payload and visual quality, making it well-suited for secure and high-performance applications. Full article

Microbial proteases are fundamental towards the eco-sustainability of proteolysis at the industrial scale. A proteolytic broth was obtained from a bioreactor fermentation of a proteolytic Bacillus strain isolated from an industrial alkaline bath. Broth proteolytic activity was applied to leather tanning and to the removal of protein stains. The hide tanned with the microbial proteolytic fermentation broth showed better physical properties than the one tanned with commercial pancreatic proteases of the same activity (780 LVU). Proteinaceous stains on cotton fabric were removed more efficiently using the Bacillus proteolytic broth than water or a commercial detergent. Blood and egg yolk disappeared in less than 30 min. The removal of soya and English sauce stains was even faster. Broth proteolytic activity was characterised by caseinolytic (5200 LVU), collagenolytic (10.0 U mg⁻¹), elastolytic (3.7 U mg⁻¹), and keratinolytic (0.7 U mg⁻¹) activities, which were compared with those of a commonly used commercial protease. Alkaline protease activity in the broth was demonstrated by a 20% increase in caseinolytic activity from pH 5 to 8. Besides the demonstrated applications in the leather and detergent industries, the produced alkaline microbial proteases can also be used in the treatment of proteinaceous wastes and effluents, offering potential environmental benefits reinforcing and impacting the bioeconomy. Full article

This paper investigates the effectiveness of lightweight Convolutional Neural Networks (CNNs) compared with Vision Transformers (ViTs) for industrial defect detection, with a focus on wind turbine blades. While ViTs have recently attracted significant attention in computer vision research, their advantages over traditional CNNs remain unclear in highly specialized industrial applications. To address this gap, a rigorous comparative study was conducted using a labeled dataset of wind turbine blade surface defects, including corrosion, craze, hide_craze, surface_attach, surface_corrosion, surface_injure, surface_oil, thunderstrike. Experimental results demonstrate that lightweight CNNs outperform ViTs in both accuracy and efficiency. Specifically, CNN-based models achieved a maximum accuracy of 98.2%, while the best-performing ViT reached only 50.6%. Beyond accuracy, CNNs also showed superior data efficiency and robustness when trained on relatively small datasets, underscoring their suitability for industrial defect detection tasks where large-scale annotated data are often unavailable. These findings highlight the continuing relevance of lightweight CNNs in industrial settings and provide practical guidance for selecting models in safety-critical applications such as wind turbine blade inspection. This paper contributes by clarifying the limitations of ViTs under industrial conditions and reinforcing the value of lightweight CNNs as a reliable and computationally efficient solution for defect detection. Full article

Encryption-then-compression (EtC) enables secure image processing while retaining coding efficiency. In grayscale-based EtC pipelines with YCbCr transformation and component serialization, reversible data hiding (RDH) becomes challenging because cross-channel correspondence is disrupted, and block-wise encryption operations (permutation, rotation, and brightness inversion) break embedding synchronization. This paper presents a block-independent and lightweight RDH framework for such component-serialized grayscale EtC systems. The framework combines diagonal pixel absolute difference (DPAD)-based embedding with an encryption-invariant synchronization index (EISI), enabling reliable encrypted-domain extraction and self-synchronization under component serialization and block permutation, without auxiliary side information or any modification to the underlying EtC pipeline. All operations are performed locally at the block level, making the framework naturally parallelizable when needed. Experiments on standard datasets with diverse texture characteristics demonstrate reliable data extraction and perfect reversibility while preserving the structural properties required for secure encryption and lossless-mode compression. These results indicate that the proposed framework is well-suited to practical EtC deployments where lightweight implementation and block-level independence are essential. Full article

As artificial intelligence (AI) becomes an integral part of organizational operations, collaboration between humans and AI is transforming employees’ work experiences and behavioral patterns. This study examines the psychological challenges and coping responses associated with such collaboration. Drawing on Cognitive Appraisal Theory, we construct and test a theoretical framework that connects employee–AI collaboration to knowledge hiding via job insecurity, while considering AI trust as a moderating variable. Data were collected through a three-wave time-lagged survey of 348 employees working in knowledge-intensive enterprises in China. The empirical results demonstrate that (1) employee–AI collaboration elevates perceptions of job insecurity; (2) job insecurity fosters knowledge-hiding behavior; (3) job insecurity mediates the link between collaboration and knowledge hiding; and (4) AI trust buffers the positive effect of collaboration on job insecurity, thereby reducing its indirect impact on knowledge hiding. These findings reveal the paradoxical role of AI collaboration: although it enhances efficiency, it may also provoke defensive reactions that inhibit knowledge exchange. By highlighting the role of AI trust in shaping employees’ cognitive appraisals, this study advances understanding of how cognitive appraisals influence human adaptation to intelligent technologies. Practical insights are offered for managers aiming to cultivate trust-based and psychologically secure environments that promote effective human–AI collaboration and organizational innovation. Full article

Motion deblurring is an ill-posed, challenging problem in image restoration due to non-uniform motion blurs. Although recent deep convolutional neural networks have made significant progress, many existing methods adopt multi-scale or multi-patch subnetworks that involve additional inter-subnetwork processing (e.g., feature alignment and fusion) across different scales or patches, leading to substantial computational cost. In this paper, we propose a novel fully-cascaded spatial-aware convolutional network (FSCNet) that effectively restores sharp images from blurry inputs while maintaining a favorable balance between restoration quality and computational efficiency. The proposed architecture consists of simple yet effective subnetworks connected through a fully-cascaded feature fusion (FCFF) module, enabling the exploitation of diverse and complementary features generated at each stage. In addition, we design a lightweight spatial-aware block (SAB), whose core component is a channel-weighted spatial attention (CWSA) module. The SAB is integrated into both the FCFF module and skip connections, enhancing feature fusion by enriching spatial detail representation. On the GoPro dataset, FSCNet achieves 33.01 dB PSNR and 0.962 SSIM, delivering comparable or higher accuracy than state-of-the-art methods such as HINet, while reducing model size by nearly 80%. Furthermore, when the GoPro-trained model is evaluated on three additional benchmark datasets (HIDE, REDS, and RealBlur), FSCNet attains the highest average PSNR (29.53 dB) and SSIM (0.903) among all compared methods. This consistent cross-dataset superiority highlights FSCNet’s strong generalization and robustness under diverse blur conditions, confirming that it achieves state-of-the-art performance with a favorable performance–complexity trade-off. Full article

Frequent data sharing in Vehicular Ad Hoc Networks (VANETs) necessitates a robust foundation of secure access control to ensure data security. Existing ciphertext-policy attribute-based encryption schemes are constrained by the performance bottleneck of a single attribute authority. Furthermore, although many schemes adopt outsourced decryption, the verifiability of the decryption results is not guaranteed. Therefore, this paper proposes a Symmetrically Verifiable Outsourced Decryption Data Sharing Scheme with Privacy-Preserving for VANETs (VODDS). To balance the computational overhead across multiple authorities, VODDS introduces a distributed key distribution mechanism that organizes them into groups. Within each group, the key distribution credential is generated through a Group Key Agreement, with each round secured by a Byzantine consensus mechanism to achieve a balance between security and efficiency. User identities are converted into anonymous representations via hashing for embedding into the attribute keys. Furthermore, blockchain technology is used to record a hash commitment for the verification ciphertext. This enables the user to verify the outsourced result through a smart contract, which performs a symmetrical verification by matching the user’s locally computed hash against the on-chain record. Moreover, VODDS employs a linear secret sharing scheme to achieve policy hiding. We provide security analysis under the q-parallel Bilinear Diffie–Hellman Exponent and Decisional Diffie–Hellman assumptions, which proves the security of VODDS. In addition, VODDS exhibits higher efficiency compared to related schemes in the performance evaluation. Full article

With the rapid proliferation of digital multimedia in resource-constrained Internet of Things (IoT) environments, there is growing demand for efficient image compression combined with secure data embedding. Existing Vector Quantization (VQ)-based Reversible Data Hiding (RDH) methods prioritize embedding capacity while neglecting reconstruction fidelity, often introducing noticeable quality degradation in edge regions—unacceptable for high-fidelity applications such as medical imaging and forensic analysis. This paper proposes a lightweight RDH framework with a once-offline trained VQ codebook that simultaneously performs secure data embedding and visual quality enhancement for VQ-compressed images. Quality Improvement Codes (QIC) are generated from pixel-wise residuals between original and VQ-decompressed images and embedded into the VQ index table using a novel Recoding Index Value (RIV) mechanism without additional transmission overhead. Sobel edge detection identifies perceptually sensitive blocks for targeted enhancement. Comprehensive experiments on ten standard test images across multiple resolutions (256 × 256, 512 × 512) and codebook sizes (64–1024) demonstrate Peak Signal-to-Noise Ratio (PSNR) gains of +4 to +5.39 dB and Structural Similarity Index Measure (SSIM) improvements of +4.12% to +9.86%, with embedding capacities approaching 100 Kbits. The proposed approach consistently outperforms existing methods in both image quality and payload capacity while eliminating computational overhead of deep learning models, making it highly suitable for resource-constrained edge devices and real-time multimedia security applications. Full article

In open-network environments of smart buildings and urban infrastructure, abnormal traffic from security and energy monitoring systems is critical for operational safety and decision reliability. We can develop malware that exploits building automation protocols to simulate attacks involving the falsification or modification of chiller controller commands, thereby endangering the entire network infrastructure. Intrusion detection systems rely on abundant labeled abnormal traffic data to detect attack patterns, improving network system reliability. However, transmitting such data faces two major challenges: single-feature representations fail to capture comprehensive traffic features, limiting the information representation for artificial intelligence (AI)-based detection models, and unconcealed abnormal traffic is easily intercepted by firewalls or intrusion detection systems, hindering cross-departmental sharing. Existing methods struggle to balance feature integrity and transmission stealth, often sacrificing one for the other or relying on easily detectable spatial-domain steganography. To address these gaps, we propose a multi-channel imaging hiding method that reconstructs abnormal traffic into multi-channel images by combining three mappings to generate grayscale images that depict traffic state transitions, dynamic trends, and internal similarity, respectively. These images are combined to enhance feature representation and embedded into frequency-domain adversarial examples, enabling evasion of security devices while preserving traffic integrity. Experimental results demonstrate that our method captures richer information than single-representation approaches, achieving a PSNR of 44.5 dB (a 6.0 dB improvement over existing methods) and an SSIM of 0.97. The high-fidelity reconstructions enabled by these gains facilitate the secure and efficient sharing of abnormal traffic data, thereby enhancing AI-driven security in smart buildings. Full article

To address the issues of image quality degradation and file size expansion encountered during reversible data hiding (RDH) of JPEG images, a JPEG reversible data hiding algorithm based on block smoothness estimation and optimal zero coefficient selection is proposed. Firstly, a block smoothness estimation strategy is designed based on the number of zero coefficients and non-zero quantisation table values within DCT blocks, prioritising DCT blocks with higher smoothness for information embedding. Subsequently, under a given embedding payload, an optimal zero coefficient selection strategy is introduced. Blocks are partitioned into embedding regions and non-embedding regions based on a preset position threshold T. Within embedding regions, the frequency of zero coefficients at different positions across all blocks is statistically analysed, with embedding prioritised at positions exhibiting the highest zero coefficient frequency to enhance embedding efficiency. Concurrently, by setting positive and negative displacement gaps to constrain the modification range of non-zero coefficients, invalid shifts are minimised. This further enhances visual quality while controlling file expansion. Experimental results demonstrate that, compared to existing algorithms, the proposed method achieves a peak signal-to-noise ratio improvement of 0.75 to 3.62 dB under fixed embedding capacity. File expansion is reduced by 1038 to 2243 bits, whilst enabling fully reversible image restoration. Full article

Anonymous identity-based encryption (AIBE) is an extension of identity-based encryption (IBE) that enhances the privacy of a ciphertext by providing ciphertext anonymity. In this paper, we introduce the concept of revocable IBE with anonymous revocation (RIBE-AR), which can issue an update key and hide the revoked set of the update key that efficiently revokes private keys of AIBE. We first define the security models of RIBE-AR and propose an efficient RIBE-AR scheme in bilinear groups. Our RIBE-AR scheme is similar to the existing RIBE scheme in terms of efficiency, but it is the first RIBE scheme to provide additional ciphertext anonymity and revocation privacy. We show that our RIBE-AR scheme provides selective message privacy, selective identity privacy, and selective revocation privacy. Full article