Reversible data hiding (RDH) techniques recover the original cover image after data extraction. Thus, they have gained popularity in e-healthcare, law forensics, and military applications. However, histogram shifting using a reversible data embedding technique suffers from low embedding capacity and high variability. This work proposes a technique in which the distribution obtained from the cover image determines the pixels that attain a peak or zero distribution. Afterward, adjacent histogram bins of the peak point are shifted, and data embedding is performed using the least significant bit (LSB) technique in the peak pixels. Furthermore, the robustness and embedding capacity are improved using the proposed dynamic block-wise reversible embedding strategy. Besides, the secret data are encrypted before embedding to further strengthen security. The experimental evaluation suggests that the proposed work attains superior stego images with a peak signal-to-noise ratio (PSNR) of more than 58 dB for 0.9 bits per pixel (BPP). Additionally, the results of the two-sample t-test and the Kolmogorov–Smirnov test reveal that the proposed work is resistant to attacks.

Recent decades have seen dramatic development and adoption of digital technology. This technological advancement generates a large amount of critical data that must be safeguarded. The security of confidential data is one of the primary concerns in fog computing. As a result, achieving a reliable level of security in the fog computing environment is crucial. To this context, 3D point and mesh fog data are becoming increasingly popular among the various types of data stored in the fog. Data encryption using chaotic behavior is one of the preferred research areas due to its unique properties such as randomness, determinism, sensitivity to initial conditions, and ergodicity. In this paper, we have taken advantage of this chaotic behavior to achieve higher security. This study presents a novel approach for protecting the privacy of 3D point and mesh fog data. Initially, the fog data coordinates are transformed using the sequence generated by the chaotic behavior. Then, bifurcation analysis is used to depict the enhanced scope of the proposed map. The quality of the proposed chaotic system is assessed using metrics such as the Lyapunov exponent and approximate entropy. Results show that the proposed encryption framework performs superior when subjected to brute-force and statistical attacks. Further, the designed framework produces better results as compared to the prior literature.

The global production of information continuously increases in quantity and variety. However, the tools and technologies developed to handle such large volumes of data have not adequately met the security and privacy requirements. Existing cloud security systems, often managed by a trusted third party, are susceptible to various security risks. To address these challenges and ensure the protection of personal information, blockchain technology emerges as a crucial solution with substantial potential. This research uses the blockchain-powered attribute-aware encryption method to establish a real-time secure communication approach over the cloud. By employing attribute-based encryption technology, data owners can implement fine-grained search permissions for data users. The proposed solution incorporates accessible encryption technology to enable secure access to encrypted data and facilitate keyword searches on the blockchain. This study provides a functional comparison of recently developed attribute-based encryption algorithms. The access control strategy comprises two access tree types and a linear secret-sharing system, serving as the main components. The elliptic curve’s base field was set to 512b, and the bilinear pairing parameter type used was Type-A. This approach involves storing keywords on a remote server and encrypting them using attribute-based encryption. Furthermore, the encrypted data blockchain and the corresponding ciphertext are stored in the blockchain. Numerical experiments were conducted to evaluate the system’s key generation, trapdoor building, and keyword retrieval capabilities.

The rapid proliferation of misinformation across digital platforms has highlighted the critical need for advanced fake news detection mechanisms. Traditional methods primarily rely on textual analysis, often neglecting the structural patterns of news dissemination, which play a crucial role in determining credibility. To address this limitation, this study proposes a Dual-Stream Graph-Augmented Transformer Model, integrating BERT for deep textual representation and Graph Neural Networks (GNNs) to model the propagation structure of misinformation. The objective is to enhance fake news detection by leveraging both linguistic and network-based features. The proposed method employs Graph Attention Networks (GAT) and Graph Transformers to extract contextual relationships, while an attention-based fusion mechanism effectively integrates textual and graph embeddings for classification. The model is implemented using PyTorch and Hugging Face Transformers, with experiments conducted on the FakeNewsNet dataset, which includes news articles, user interactions, and source metadata. Evaluation metrics such as accuracy, precision, recall, F1-score, and AUC-ROC indicate superior performance, with an accuracy of 99%, outperforming baseline models such as Bi-LSTM and RoBERTa-GCN. The study concludes that incorporating graph-based propagation features significantly improves fake news detection, providing a robust, scalable, and context-aware solution. Future enhancements will focus on refining credibility assessment mechanisms and extending the model to support multilingual and multimodal misinformation detection across diverse digital platforms.

Electronic commerce (E-commerce) transactions require secure communication to protect sensitive information such as credit card numbers, personal identification, and financial data from unauthorized access and fraud. Encryption using public key cryptography is essential to ensure secure electronic commerce transactions. RSA and Rabin cryptosystem algorithms are widely used public key cryptography techniques, and their security is based on the assumption that it is computationally infeasible to factorize the product of two large prime numbers into its constituent primes. However, existing variants of RSA and Rabin cryptosystems suffer from issues like high computational complexity, low speed, and vulnerability to factorization attacks. To overcome the issue, this article proposes a new method that introduces the concept of fake-modulus during encryption. The proposed method aims to increase the security of the Rabin cryptosystem by introducing a fake-modulus during encryption, which is used to confuse attackers who attempt to factorize the public key. The fake-modulus is added to the original modulus during encryption, and the attacker is unable to distinguish between the two. As a result, the attacker is unable to factorize the public key and cannot access the sensitive information transmitted during electronic commerce transactions. The proposed method’s performance is evaluated using qualitative and quantitative measures. Qualitative measures such as visual analysis and histogram analysis are used to evaluate the proposed system’s quality. To quantify the performance of the proposed method, the entropy of a number of occurrences for the pixels of cipher text and differential analysis of plaintext and cipher text is used. When the proposed method’s complexity is compared to a recent variant of the Rabin cryptosystem, it can be seen that it is more complex to break the proposed method—represented as $\mathrm{O}\left(ɲ\times \mathsf{\tau }\right)$which is higher than Rabin-P ($\mathrm{O}(ɲ))$ algorithms.

RSA is one among the most popular public key cryptographic algorithm for security systems. It is explored in the results that RSA is prone to factorization problem, since it is sharing common modulus and public key exponent. In this paper the concept of fake modulus and generalized Pell’s equation is used for enhancing the security of RSA. Using generalized Pell’s equation it is explored that public key exponent depends on several parameters, hence obtaining private key parameter itself is a big challenge. Fake modulus concept eliminates the distribution of common modulus, by replacing it with a prime integer, which will reduce the problem of factorization. It also emphasizes the algebraic cryptanalysis methods by exploring Fermat’s factorization, Wiener’s attack, and Trial and division attacks.