Optimization of Asymmetric and Symmetric Algorithms

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 627

Special Issue Editors


E-Mail Website
Guest Editor
Department of Information Engineering, Marches Polytechnic University, Via Brecce Bianche, I-60131 Ancona, Italy
Interests: modeling and simulation of complex aircrafts; modeling, simulation and control of complex underwater vehicles; modeling, simulation and control of complex spacecrafts; simulation and control of DC2DC power converters
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: electrical automation; information engineering

E-Mail Website
Guest Editor
School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601, China
Interests: optical remote sensing target detection; infrared target tracking; image semantic segmentation; scene understanding

Special Issue Information

Dear Colleague,

Asymmetric and symmetric study on algorithm optimization refers to the analysis and comparison of different optimization techniques used for asymmetric and symmetric algorithms.

  1. Asymmetric Algorithms:

Asymmetric algorithms, also known as public-key algorithms, use two different keys—a public key and a private key. These algorithms are widely used for encryption and decryption purposes, digital signatures, and key exchange. Asymmetric algorithms are slower compared to symmetric algorithms due to the complex mathematical operations involved.

Optimization techniques for asymmetric algorithms focus on reducing the computational overhead and improving performance. Some common optimization techniques include the following:

  • Efficient implementation of modular exponentiation operations using algorithms like square-and-multiply, Montgomery multiplication, and sliding window;
  • Utilizing hardware acceleration or specialized hardware (such as cryptographic coprocessors) for modular arithmetic operations;
  • Implementing efficient key generation and management processes to reduce the complexity and time overhead;
  • Employing precomputation techniques to save computational time for frequently used operations.

The study of asymmetric algorithm optimization involves analyzing the impact of these techniques on the overall performance, security, and efficiency of the algorithm.

  1. Symmetric Algorithms:

Symmetric algorithms use a single shared key for both encryption and decryption processes. These algorithms are generally faster compared to asymmetric algorithms as they involve simpler mathematical operations. Symmetric algorithms are widely used in scenarios where speed and efficiency are crucial, such as data encryption in real-time communication or large-scale data processing.

Optimization techniques for symmetric algorithms aim to further enhance their performance and efficiency. Some common optimization techniques include the following:

  • Implementing parallel processing techniques to take advantage of modern multi-core processors and GPUs;
  • Optimizing memory usage by employing efficient data structures and algorithms, such as hashing, indexing, or compression techniques;
  • Utilizing lookup tables or pre-calculated values for frequently used operations to reduce computational overhead;
  • Employing hardware acceleration techniques, such as AES-NI instructions, to offload cryptographic operations to specialized hardware.

The study of symmetric algorithm optimization involves analyzing the impact of these techniques on the algorithm's speed, security, memory usage, and resource utilization.

Overall, the study of asymmetric and symmetric algorithm optimization involves exploring different techniques and trade-offs to improve the performance, efficiency, and security of both types of algorithms.

Prof. Dr. Simone Fiori
Dr. Di Wang
Dr. Shuo Zhuang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cryptography
  • encryption
  • decryption
  • asymmetric algorithms
  • symmetric algorithms
  • key generation
  • key management
  • public key cryptography
  • private key cryptography
  • digital signatures
  • hash functions
  • block ciphers

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

15 pages, 3340 KiB  
Article
A Quantitative Precipitation Estimation Method Based on 3D Radar Reflectivity Inputs
by Yanqin Wen, Jun Zhang, Di Wang, Xianming Peng and Ping Wang
Symmetry 2024, 16(5), 555; https://doi.org/10.3390/sym16050555 - 3 May 2024
Viewed by 351
Abstract
Quantitative precipitation estimation (QPE) by radar observation data is a crucial aspect of meteorological forecasting operations. Accurate QPE plays a significant role in mitigating the impact of severe convective weather. Traditional QPE methods mainly employ an exponential Z–R relationship to map the radar [...] Read more.
Quantitative precipitation estimation (QPE) by radar observation data is a crucial aspect of meteorological forecasting operations. Accurate QPE plays a significant role in mitigating the impact of severe convective weather. Traditional QPE methods mainly employ an exponential Z–R relationship to map the radar reflectivity to precipitation intensity on a point-to-point basis. However, this isolated point-to-point transformation lacks an effective representation of convective systems. Deep learning-based methods can learn the evolution patterns of convective systems from rich historical data. However, current models often rely on 2 km-height CAPPI images, which struggle to capture the complex vertical motions within convective systems. To address this, we propose a novel QPE model: combining the classic extrapolation model ConvLSTM with Unet for an encoder-decoder module assembly. Meanwhile, we utilize three-dimensional radar echo images as inputs and introduce the convolutional block attention module (CBAM) to guide the model to focus on individual cells most likely to trigger intense precipitation, which is symmetrically built on both channel and spatial attention modules. We also employ asymmetry in training using weighted mean squared error to make the model concentrate more on heavy precipitation events which are prone to severe disasters. We conduct experiments using radar data from North China and Eastern China. For precipitation above 1 mm, the proposed model achieves 0.6769 and 0.7910 for CSI and HSS, respectively. The results indicate that compared to other methods, our model significantly enhances precipitation prediction accuracy, with a more pronounced improvement in forecasting accuracy for heavy precipitation events. Full article
(This article belongs to the Special Issue Optimization of Asymmetric and Symmetric Algorithms)
Show Figures

Figure 1

Back to TopTop