In recent years, research on attribute-based encryption (ABE) has expanded into the quantum domain. Because a traditional single authority can cause the potential single point of failure, an improved lattice-based quantum-resistant identity authentication and policy attribute encryption scheme is proposed, in which the generation of random values is optimized by adjusting parameters in the Gaussian sampling algorithm to improve overall performance. Additionally, in the key generation phase, attributes are processed according to their shared nature, which reduces the computational overhead of the authorization authority. In the decryption phase, the basis transformation of the Lenstra-Lenstra-Lovász (LLL) lattice reduction algorithm is utilized to rapidly convert shared matrices into the shortest vector form, which can reduce the computational cost of linear space checks. The experimental results demonstrate that the proposed method not only improves efficiency but also enhances security compared with related schemes.
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http://dx.doi.org/10.3390/e26090729 | DOI Listing |
Acad Med
December 2024
R.H. Kon is associate professor of medicine, Department of Medicine, University of Virginia School of Medicine, Charlottesville, Virginia; ORCID: https://orcid.org/0000-0002-3326-5203.
J Agric Food Chem
January 2025
Escuela de Ingenieria y Ciencias, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey, Nuevo Leon 64849, Mexico.
Fatty acid (FA), tocopherol, and phytosterol profiles are used in avocado oil purity standards. However, blends with other oils can mimic the profile of pure avocado oil, resulting in similar ranges for these molecules. Therefore, fatty alcohol esters (FAEs) uniquely of spp.
View Article and Find Full Text PDFFront Digit Health
December 2024
Computer Science Department, Carlos III University of Madrid, Getafe, Spain.
Network
December 2024
Department of Electronics and Communication Engineering, Dronacharya Group of Institutions, Greater Noida, UP, India.
Speaker verification in text-dependent scenarios is critical for high-security applications but faces challenges such as voice quality variations, linguistic diversity, and gender-related pitch differences, which affect authentication accuracy. This paper introduces a Gender-Aware Siamese-Triplet Network-Deep Neural Network (ST-DNN) architecture to address these challenges. The Gender-Aware Network utilizes Convolutional 2D layers with ReLU activation for initial feature extraction, followed by multi-fusion dense skip connections and batch normalization to integrate features across different depths, enhancing discrimination between male and female speakers.
View Article and Find Full Text PDFAcad Med
October 2024
T.H. Champney is professor, Department of Cell Biology, University of Miami Miller School of Medicine, Miami, Florida; ORCID: https://orcid.org/0000-0002-0507-1663.
A new ethos of anatomy education goes beyond the learning of body parts in the traditional curriculum. In the traditional curriculum, the focus of only providing information on the structure of the human body left certain learning opportunities overlooked, marginalized, or dismissed as irrelevant; thus, opportunities to foster and shape professional attributes in health care learners were lost. Furthermore, changes in curricula structures and reductions in anatomy teaching hours have necessitated a transformation in how anatomy education is perceived and delivered.
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