scholarly journals The Improvement of Elliptic Curve Factorization Method to Recover RSA’s Prime Factors

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1314
Author(s):  
Kritsanapong Somsuk

Elliptic Curve Factorization Method (ECM) is the general-purpose factoring method used in the digital computer era. It is based on the medium length of the modulus; ECM is an efficient algorithm when the length of modulus is between 40 and 50 digits. In fact, the main costs for each iteration are modular inverse, modular multiplication, modular square and greatest common divisor. However, when compared to modular multiplication and modular square, the costs of modular inverse and greatest common divisor are very high. The aim of this paper is to improve ECM in order to reduce the costs to compute both of modular inverse and greatest common divisor. The proposed method is called Fast Elliptic Curve Factorization Method (F-ECM). For every two adjacent points on the curve, only one modular inverse and one greatest common divisor will be computed. That means it implies that the costs in both of them can be split in half. Furthermore, the length of modulus in the experiment spans from 30 to 65 bits. The experimental results show that F-ECM can finish the task faster than ECM for all cases of the modulus. Furthermore, the computation time is reduced by 30 to 38 percent.

2017 ◽  
Vol 2 (3) ◽  
pp. 32-39
Author(s):  
Aya Khalid Naji ◽  
Saad Najim Alsaad

In the development of 3G devices, all elements of multimedia (text, image, audio, and video) are becoming crucial choice for communication. The secured system in 3G devices has become an issue of importance, on which lot of research is going on. The traditional cryptosystem like DES, AES, and RSA do not able to meet with the properties of the new generation of digital mobile devices. This paper presents an implementation of video protection of fully encrypted using Elliptic Curve   Cryptography (ECC) on a mobile device. The Android platform is used for this purpose.  The results refer that the two important criteria of video mobile encryption: the short computation time required and high confidentially are provided.


2021 ◽  
Author(s):  
Ho Yin Yuen ◽  
Jesper Jansson

Abstract Background: Protein-protein interaction (PPI) data is an important type of data used in functional genomics. However, inaccuracies in high-throughput experiments often result in incomplete PPI data. Computational techniques are thus used to infer missing data and to evaluate confidence scores, with link prediction being one such approach that uses the structure of the network of PPIs known so far to find good candidates for missing PPIs. Recently, a new idea called the L3 principle introduced biological motivation into PPI link predictions, yielding predictors that are superior to general-purpose link predictors for complex networks. However, the previously developed L3 principle-based link predictors are only an approximate implementation of the L3 principle. As such, not only is the full potential of the L3 principle not realized, they may even lead to candidate PPIs that otherwise fit the L3 principle being penalized. Result: In this article, we propose a formulation of link predictors without approximation that we call ExactL3 (L3E) by addressing missing elements within L3 predictors in the perspective of network modeling. Through statistical and biological metrics, we show that in general, L3E predictors perform better than the previously proposed methods on seven datasets across two organisms (human and yeast) using a reasonable amount of computation time. In addition to L3E being able to rank the PPIs more accurately, we also found that L3-based predictors, including L3E, predicted a different pool of real PPIs than the general-purpose link predictors. This suggests that different types of PPIs can be predicted based on different topological assumptions and that even better PPI link predictors may be obtained in the future by improved network modeling.


1995 ◽  
Vol 68 (3) ◽  
pp. 461-480 ◽  
Author(s):  
A. J. Tinker

Abstract All of the blend systems studied to date show a tendency to have an uneven distribution of crosslinks between the phases; this must be taken to be the norm, with instances of near-even crosslinking being the exception. When the crosslink distribution has been made to be near even, the properties investigated are generally improved. In blends of polar and nonpolar elastomers, there is a tendency for the polar elastomer to be more highly crosslinked than the less polar elastomer, because both the sulfur and accelerators partition in favor of the former. However, if one elastomer has a very high solubility parameter, the accelerator may partition in favor of the less polar elastomer. Provided this differential partition of the curatives is not extreme, the results can be beneficial. There is one recorded instance of a drastic reduction in overall crosslink density through very low crosslinking of one phase, and this may be ascribed to strong differential partition. Since crosslink distribution in blends of elastomers differing in solubility parameter appears to be controlled largely by partition of curatives and vulcanization intermediates, influence may be exerted by appropriate choice of accelerator(s). The crosslink distribution in blends of low and high unsaturation elastomers is extreme; at normal levels of curatives, the low unsaturation elastomer may be virtually uncrosslinked whilst the high unsaturation elastomer can have a higher crosslink density than is desirable. Modification of EPDM to either form an independent network in the EPDM phase or enforce the formation of sulfur crosslinks during vulcanization is effective. The level of crosslinking required in the EPDM phase in order to provide substantial improvements in properties of blends is modest, much lower than would be required to give acceptable properties in a single polymer vulcanizate. Blends of general purpose elastomers, such as NR and BR, do not differ significantly in either solubility parameter or degree of unsaturation and are generally considered not to be problematic. However, these often show an uneven distribution of crosslinks. Studies of the progress of crosslinking during vulcanization suggest that partition of curatives could play a role even here. The detailed view of crosslink distribution presented here is largely due to the success of a swollen-state NMR spectroscopy technique, and there is considerable scope for further advances in view of the list of combinations of elastomers which may be investigated in this way. The list is reproduced in Table IX, suitably updated to take account of recent developments. Further advances are expected; not just in respect of the investigation of new blend and curative systems, but also in the combination of the swollen-state NMR technique with other established procedures such as the use of chemical probe reagents to provide information on the type of sulfur crosslinks present in each phase of a blend.


Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3341 ◽  
Author(s):  
Hilal Tayara ◽  
Kil Chong

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it is a challenging task due to the different scales and appearances of the objects. On the other hand, object detection task in VHR aerial images has improved remarkably in recent years due to the achieved advances in convolution neural networks (CNN). Most of the proposed methods depend on a two-stage approach, namely: a region proposal stage and a classification stage such as Faster R-CNN. Even though two-stage approaches outperform the traditional methods, their optimization is not easy and they are not suitable for real-time applications. In this paper, a uniform one-stage model for object detection in VHR aerial images has been proposed. In order to tackle the challenge of different scales, a densely connected feature pyramid network has been proposed by which high-level multi-scale semantic feature maps with high-quality information are prepared for object detection. This work has been evaluated on two publicly available datasets and outperformed the current state-of-the-art results on both in terms of mean average precision (mAP) and computation time.


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