Search Results (38)

To ensure confidentiality and integrity in the era of quantum computing, most post-quantum cryptographic schemes are designed to achieve either encryption or digital signature functionalities separately. Although a few signcryption schemes exist that combine these operations into a single, more efficient process, they typically rely on complex vector, matrix, or polynomial-based structures. In this work, a novel post-quantum public-key encryption and signature (PQES) scheme based entirely on scalar integer operations is presented. The proposed scheme employs a simplified structure where the ciphertext, keys, and core cryptographic operations are defined over scalar integers modulo n, significantly reducing computational and memory overhead. By avoiding high-dimensional lattices or ring-based constructions, the PQES approach enhances implementability on constrained devices while maintaining strong security properties. The design is inspired by modified learning-with-errors (LWE) assumptions, adapted to scalar settings, making it suitable for post-quantum applications. Security and performance evaluations, achieving a signcryption time of 0.0007 s and an unsigncryption time of 0.0011 s, demonstrate that the scheme achieves a practical balance between efficiency and resistance to quantum attacks. Full article

This article investigates the Q-Problem, a novel theoretical framework for post-quantum cryptography. It aims to redefine cryptographic hardness by moving away from problems with unique solutions toward problems that admit multiple indistinguishable preimages. This shift is motivated by the structural vulnerabilities that quantum algorithms may exploit in traditional formulations. To support this paradigm, we define new cryptographic primitives and security notions, including Q-Indistinguishability, Long-Term Secrecy, and a spectrum of Q-Secrecy levels. The methodology formalizes the Q-Problem as a system of expressions, called Q-expressions, that must satisfy a set of indistinguishability and reduction properties. We also propose a taxonomy of its models, including Connected/Disconnected, Totally/Partly, Fully/Partially Probabilistic, Perfect, and Ideal Q-Problem variants. These models illustrate the versatility across a range of cryptographic settings. By abstracting hardness through indistinguishability rather than solvability, Q-Problem offers a new direction for designing cryptographic protocols resilient to future quantum attacks. This foundational framework provides the foundations for long-term, composable, and structure-aware security in the quantum era. Full article

The rapid growth of Internet of Things (IoT) devices across industrial and critical sectors requires robust and efficient authentication mechanisms. Traditional authentication systems struggle to balance security, privacy and computational efficiency, particularly in resource-constrained environments such as Industry 4.0. This research presents the SmartIoT Hybrid Machine Learning (ML) Model, a novel integration of Attribute-Based Authentication and a lightweight machine learning algorithm designed to enhance security while minimising computational overhead. The SmartIoT Hybrid ML Model utilises Random Forest classifiers for real-time anomaly detection, dynamically assessing access requests based on user attributes, login patterns and behavioural analysis. The model enhances identity protection while enabling secure authentication without exposing sensitive information by incorporating privacy-preserving Attribute-Based Credentials and Attribute-Based Signatures. Our experimental evaluation demonstrates 86% authentication accuracy, 88% precision and 96% recall, significantly outperforming existing solutions while maintaining an average response time of 112ms, making it suitable for low-power IoT devices. Comparative analysis with state-of-the-art authentication frameworks shows the model’s security resilience, computational efficiency and adaptability in real-world IoT applications. Full article

The Q-problem is a new lightweight and hard mathematical problem that resists quantum attacks. It depends on putting one known value and two unknown values per equation; whatever the operator, the Q-problem defines certain conditions between equations. This paper presents a new key exchange protocol based on the Q-problem. To protect secure end-to-end communication over a public transmission channel, the proposed mechanism consists of two rounds of exchanging totally random numbers, which ensure a shared secret key between two parties at the end. Security analysis proves the robustness of the proposal and experiments prove its lightness during implementation, making it a promising protocol of hybrid solutions and an assistive technique for the transition to the quantum era. Full article

Background and Objectives: Atrial fibrillation (AF) carries a huge socioeconomic burden as it is the most encountered cardiac arrhythmia with a significant morbidity. Echocardiographic (Echo) imaging is of monumental value in providing insight into assessing the cardiac function and anatomy, etiology, and risk stratification of AF patients, which will ultimately lead to the best management plan. Materials and Methods: A total of 2160 adult patients diagnosed with AF in 18 hospitals and 30 out-patient cardiology clinics in Jordan and 1 hospital in the Palestinian Territories were enrolled in this study from May 2019 to January 2021. Ultimately, 1776 patients were included in the analysis after going through the exclusion criteria. Results: The majority of our participants were found to have normal EF at the time of enrollment, with only 31.6% exhibiting a decreased EF. Only 40% of overall patients had Echo evidence of left ventricular hypertrophy (LVH). These patients were older (70.27 ± 10.1 vs. 66.0 ± 14.3, p < 0.001), more obese (45.2% vs. 37.3%, p-value < 0.001), and had a more frequent occurrence of HTN (89.0% vs. 65.6%, p < 0.001) and DM (49.2% vs. 40.1%, p < 0.001) when compared to patients without LVH. A proportion of 84.2% of female patients had abnormal left atrial (LA) size (>3.8 cm), in contrast to only 53.4% of males (LA > 4.2 cm). Pulmonary hypertension (PH) was only observed in 27.9% of our patients, and when comparing patients with PH vs. patients without PH, decreased EF (<50%) (36.9% vs. 20.6%, p = 0.001), a higher prevalence of OSA (6.7% vs. 3.8%, p = 0.009), female predominance (60.3% vs. 39.7%, p < 0.001), and older age (70.2 ± 10.7 vs. 66.7 ± 13.6, p < 0.001) were observed in patients with PH. Conclusion: This study provides the first reported insights on the atrial fibrillation-related echocardiographic findings in a Middle Eastern population. Notably, our study demonstrates that the majority of the studied population have no evidence of LVH and have preserved EF on baseline. However, LA enlargement was extremely frequent among females but not in males, warranting further evaluation to determine the factors contributing to such a difference. Full article

Identifying vulnerabilities in Smart Contracts (SCs) is crucial, as they can lead to significant financial losses if exploited. Although various SC vulnerability identification methods exist, selecting the most effective approach remains challenging. This article examines these challenges and introduces solutions to enhance SC vulnerability identification. It introduces MultiTagging, a modular SC multi-labeling framework designed to overcome limitations in existing SC vulnerability identification approaches. MultiTagging automates SC vulnerability tagging by parsing analysis reports and mapping tool-specific tags to standardized labels, including SC Weakness Classification (SWC) codes and Decentralized Application Security Project (DASP) ranks. Its mapping strategy and the proposed vulnerability taxonomy resolve tool-level labeling inconsistencies, where different tools use distinct labels for identical vulnerabilities. The framework integrates an evaluation module to assess SC vulnerability identification methods. MultiTagging enables both tool-based and vote-based SC vulnerability labeling. To improve labeling accuracy, the article proposes Power-based voting, a method that systematically defines voter roles and voting thresholds for each vulnerability. MultiTagging is used to evaluate labeling across six tools: MAIAN, Mythril, Semgrep, Slither, Solhint, and VeriSmart. The results reveal high coverage for Mythril, Slither, and Solhint, which identified eight, seven, and six DASP classes, respectively. Tool performance varied, underscoring the impracticality of relying on a single tool to identify all vulnerability classes. A comparative evaluation of Power-based voting and two threshold-based methods—AtLeastOne and Majority voting—shows that while voting methods can increase vulnerability identification coverage, they may also reduce detection performance. Power-based voting proved more effective than pure threshold-based methods across all vulnerability classes. Full article

Online medical forums have emerged as vital platforms for patients to share their experiences and seek advice, providing a valuable, cost-effective source of feedback for medical service management. This feedback not only measures patient satisfaction and improves health service quality but also offers crucial insights into the effectiveness of medical treatments, pain management strategies, and alternative therapies. This study systematically identifies and categorizes key aspects of patient experiences, emphasizing both positive and negative sentiments expressed in their narratives. We collected a dataset of approximately 15,000 entries from various sections of the widely used medical forum, patient.info. Our innovative approach integrates content analysis with aspect-based sentiment analysis, deep learning techniques, and a large language model (LLM) to analyze these data. Our methodology is designed to uncover a wide range of aspect types reflected in patient feedback. The analysis revealed seven distinct aspect types prevalent in the feedback, demonstrating that deep learning models can effectively predict these aspect types and their corresponding sentiment values. Notably, the LLM with few-shot learning outperformed other models. Our findings enhance the understanding of patient experiences in online forums and underscore the utility of advanced analytical techniques in extracting meaningful insights from unstructured patient feedback, offering valuable implications for healthcare providers and medical service management. Full article

Recent studies have highlighted the growing integration of Natural Language Processing (NLP) techniques and Large Language Models (LLMs) in healthcare. These technologies have shown promising outcomes across various healthcare tasks, especially in widely studied languages like English and Chinese. While NLP methods have been extensively researched, LLM applications in healthcare represent a developing area with significant potential. However, the successful implementation of LLMs in healthcare requires careful review and guidance from human experts to ensure accuracy and reliability. Despite their emerging value, research on NLP and LLM applications for Arabic remains limited particularly when compared to other languages. This gap is largely due to challenges like the lack of suitable training datasets, the diversity of Arabic dialects, and the language’s structural complexity. In this study, a panel of medical experts evaluated responses generated by LLMs, including ChatGPT, for Arabic healthcare inquiries, rating their accuracy between 85% and 90%. After fine tuning ChatGPT with data from the Altibbi platform, accuracy improved to a range of 87% to 92%. This study demonstrates the potential of LLMs in addressing Arabic healthcare queries especially in interpreting questions across dialects. It highlights the value of LLMs in enhancing healthcare communication within the Arabic-speaking world and points to a promising area for further research. This work establishes a foundation for optimizing NLP and LLM technologies to achieve greater linguistic and cultural adaptability in global healthcare settings. Full article

Quantum-secure cryptography is a dynamic field due to its crucial role in various domains. This field aligns with the ongoing efforts in data security. Post-quantum encryption (PQE) aims to counter the threats posed by future quantum computers, highlighting the need for further improvement. Based on the learning with error (LWE) system, this paper introduces a novel asymmetric encryption technique that encrypts entire messages of n bits rather than just 1 bit. This technique offers several advantages including an additive homomorphic cryptosystem. The robustness of the proposed lightweight public key encryption method, which is based on a new version of LWE, ensures that private keys remain secure and that original data cannot be recovered by an attacker from the ciphertext. By improving encryption and decryption execution time—which achieve speeds of 0.0427 ms and 0.0320 ms, respectively—and decreasing ciphertext size to 708 bits for 128-bit security, the obtained results are very promising. Full article

In the rapidly advancing landscape of digital technologies, clustering plays a critical role in the domains of artificial intelligence and big data. Clustering is essential for extracting meaningful insights and patterns from large, intricate datasets. Despite the efficacy of traditional clustering techniques in handling diverse data types and sizes, they encounter challenges posed by the increasing volume and dimensionality of data, as well as the complex structures inherent in high-dimensional spaces. This research recognizes the constraints of conventional clustering methods, including sensitivity to initial centroids, dependence on prior knowledge of cluster counts, and scalability issues, particularly in large datasets and Internet of Things implementations. In response to these challenges, we propose a K-level clustering algorithm inspired by the collective behavior of fish locomotion. K-level introduces a novel clustering approach based on greedy merging driven by distances in stages. This iterative process efficiently establishes hierarchical structures without the need for exhaustive computations. K-level gives users enhanced control over computational complexity, enabling them to specify the number of clusters merged simultaneously. This flexibility ensures accurate and efficient hierarchical clustering across diverse data types, offering a scalable solution for processing extensive datasets within a reasonable timeframe. The internal validation metrics, including the Silhouette Score, Davies–Bouldin Index, and Calinski–Harabasz Index, are utilized to evaluate the K-level algorithm across various types of datasets. Additionally, comparisons are made with rivals in the literature, including UPGMA, CLINK, UPGMC, SLINK, and K-means. The experiments and analyses show that the proposed algorithm overcomes many of the limitations of existing clustering methods, presenting scalable and adaptable clustering in the dynamic landscape of evolving data challenges. Full article

Multi-agent systems (MASs) have gained a lot of interest recently, due to their ability to solve problems that are difficult or even impossible for an individual agent. However, an important procedure that needs attention in designing multi-agent systems, and consequently applications that utilize MASs, is achieving a fair agreement between the involved agents. Researchers try to prevent agreement manipulation by utilizing decentralized control and strategic voting. Moreover, emphasis is given to local decision making and perception of events occurring locally. This manuscript presents a novel secure decision-support algorithm in a multi-agent system that aims to ensure the system’s robustness and credibility. The proposed consensus-based model can be applied to production planning and control, supply chain management, and product design and development. The algorithm considers an open system; i.e., the number of agents present can be variable in each procedure. While a group of agents can make different decisions during a task, the algorithm chooses one of these decisions in a way that is logical, safe, efficient, fast, and is not influenced by factors that might affect production. Full article

The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading. Full article

Atherosclerotic cardiovascular disease (ASCVD) is the primary contributor to global mortality rates, which significantly escalates healthcare expenditures. Risk factors for ASCVD (including dyslipidemia) frequently present in clusters rather than separately. Addressing these risk factors is crucial in the early initiation of a comprehensive management plan that involves both lifestyle modifications and pharmacotherapy to reduce the impact of ASCVD. A team of Jordanian professionals from various medical organizations and institutes took the initiative to create a set of guidelines for dyslipidemia screening and therapy. A detailed, comprehensive literature review was undertaken utilizing several databases and keywords. This consensus statement provides recommendations for dyslipidemia management in Jordanians on several issues including cardiovascular risk estimation, screening eligibility, risk categories, treatment goals, lifestyle changes, and statin and non-statin therapies. It is recommended that all Jordanian individuals aged 20 years old or older undergo lipid profile testing. This should be followed by determining the level of cardiovascular risk depending on the presence or absence of ASCVD and cardiovascular risk factors, eligibility for lipid-lowering therapy, and the target low-density cholesterol serum level to be achieved. In conclusion, prioritizing the management of dyslipidemia is of the utmost importance in improving public health and reducing the burden of cardiovascular diseases. Full article

In today’s hyperconnected world, the Internet of Things and Cloud Computing complement each other in several areas. Cloud Computing provides IoT systems with an efficient and flexible environment that supports application requirements such as real-time control/monitoring, scalability, fault tolerance, and numerous security services. Hardware and software limitations of IoT devices can be mitigated using the massive on-demand cloud resources. However, IoT cloud-based solutions pose some security and privacy concerns, specifically when an untrusted cloud is used. This calls for strong encryption schemes that allow operations on data in an encrypted format without compromising the encryption. This paper presents an asymmetric multi-key and partially homomorphic encryption scheme. The scheme provides the addition operation by encrypting each decimal digit of the given integer number separately using a special key. In addition, data integrity processes are performed when an untrusted third party performs homomorphic operations on encrypted data. The proposed work considers the most widely known issues like the encrypted data size, slow operations at the hardware level, and high computing costs at the provider level. The size of generated ciphertext is almost equal to the size of the plaintext, and order-preserving is ensured using an asymmetrical encryption version. Full article