Asymmetric and Symmetric Study on Algorithms Optimization

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1468

Special Issue Editors

Department of Production Engineering, Faculty of Engineering, University Federal Fluminense, Niteroi, Brazil
Interests: operational research; multicriteria; ELECTRE I, III, IV; PROMETHEE; latent dirichlet allocation; text mining; topic model; human resources; police; police education; public security
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Dr. Valdecy Pereira
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Guest Editor
Department of Production Engineering, Faculty of Engineering, University Federal Fluminense, Niteroi, Brazil
Interests: decision making; ELECTRE method; multiple criteria

Special Issue Information

Dear Colleagues,

Asymmetric Study:

In asymmetric algorithm optimization, the focus is on leveraging the unique strengths or characteristics of the different components or entities involved. This approach aims to maximize optimization by exploiting specific advantages of individual elements rather than trying to achieve balance across all components. Asymmetric study involves identifying and utilizing the specific capabilities or resources of each entity to enhance overall performance. This approach is suitable for problems where each entity has different characteristics or capabilities that can be harnessed for optimized solutions.

Symmetric Study:

Symmetric algorithm optimization focuses on achieving balanced or equally efficient solutions across multiple components or entities. The aim is to distribute resources, workloads, or computational tasks evenly to ensure fairness, equal processing time, and optimal utilization of resources. Techniques such as load balancing, parallel computing, and task scheduling are often used in symmetric study to achieve optimal performance across all entities involved. This approach is appropriate when the components or entities are homogeneous and do not possess distinct advantages or differences in capabilities.

In both asymmetric and symmetric algorithm optimization, the choice depends on the specific problem, the characteristics of the entities involved, and the desired outcome. By understanding these approaches, researchers and practitioners can select the most suitable strategy to achieve efficient and optimized algorithms.

Prof. Dr. Marcio Basilio
Dr. Valdecy Pereira
Guest Editors

Manuscript Submission Information

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Keywords

  • symmetric algorithms
  • balanced optimization
  • equally efficient solutions
  • resource distribution
  • fairness
  • parallel computing
  • load balancing
  • task scheduling
  • homogeneous components
  • optimal performance

Published Papers (1 paper)

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Research

26 pages, 3314 KiB  
Article
Lightweight Computational Complexity Stepping Up the NTRU Post-Quantum Algorithm Using Parallel Computing
Symmetry 2024, 16(1), 12; https://doi.org/10.3390/sym16010012 - 21 Dec 2023
Viewed by 925
Abstract
The Nth-degree Truncated polynomial Ring Unit (NTRU) is one of the famous post-quantum cryptographic algorithms. Researchers consider NTRU to be the most important parameterized family of lattice-based public key cryptosystems that has been established to the IEEE P1363 standards. Lattice-based protocols necessitate operations [...] Read more.
The Nth-degree Truncated polynomial Ring Unit (NTRU) is one of the famous post-quantum cryptographic algorithms. Researchers consider NTRU to be the most important parameterized family of lattice-based public key cryptosystems that has been established to the IEEE P1363 standards. Lattice-based protocols necessitate operations on large vectors, which makes parallel computing one of the appropriate solutions to speed it up. NTRUEncrypt operations contain a large amount of data that requires many repetitive arithmetic operations. These operations make it a strong candidate to take advantage of the high degree of parallelism. The main costly operation that is repeated in all NTRU algorithm steps is polynomial multiplication. In this work, a Parallel Post-Quantum NTRUEncrypt algorithm called PPQNTRUEncrypt is proposed. This algorithm exploits the capabilities of parallel computing to accelerate the NTRUEncrypt algorithm. Both analytical and Apache Spark simulation models are used. The proposed algorithm enhanced the NTRUEncrypt algorithm by approximately 49.5%, 74.5%, 87.6%, 92.5%, 93.4%, and 94.5%, assuming that the number of processing elements is 2, 4, 8, 12, 16, and 20 respectively. Full article
(This article belongs to the Special Issue Asymmetric and Symmetric Study on Algorithms Optimization)
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