OpenCL and CUDA software implementations of encryption/decryption algorithms for IPsec VPNs

2016 ◽  
Author(s):  
Colleen Heinemann ◽  
Sai Shankar Chaduvu ◽  
Adam Byerly ◽  
Alexander Uskov
Keyword(s):  
Software Implementations ◽  
Encryption Decryption

scholarly journals DIVERSITY AES IN MIXCOLUMNS STEP WITH 8X8 CIRCULANT MATRIX

2021 ◽  
Vol 8 (9) ◽  
pp. 19-35
Author(s):  
Yan-Wen Chen ◽  
Jeng-Jung Wang ◽  
Yan-Haw Chen ◽  
Chong-Dao Lee
Keyword(s):  
Embedded Systems ◽  
Efficient Method ◽  
Matrix Multiplication ◽  
Circulant Matrix ◽  
Circulant Matrices ◽  
Matrix Operation ◽  
Branch Number ◽  
Involutory Matrix ◽  
Software Implementations ◽  
Encryption Decryption

In AES MixColumns operation, the branch number of circulant matrix is raised from 5 to 9 with 8´8 circulant matrices that can be enhancing the diffusion power. An efficient method to compute the circulant matrices in AES MixColumns transformation for speeding encryption is presented. Utilizing 8´8 involutory matrix multiplication is required 64 multiplications and 56 additions in in AES Mix-Columns transformation. We proposed the method with diversity 8´8 circulant matrices is only needed 19 multiplications and 57 additions. It is not only to encryption operations but also to decryption operations. Therefore, 8´8 circlant matrix operation with AES key sizes of 128bits, 192bits, and 256 bits are above 29.1%, 29.3%, and 29.8% faster than using 4´4 involutory matrix operation (16 multiplications, 12 additions), respectively. 8´8 circulant matrix encryption/decryption speed is above 78% faster than 8´8 involutory matrix operation. Ultimately, the proposed method for evaluating matrix multiplication can be made regular, simple and suitable for software implementations on embedded systems.

scholarly journals Analysis and software implementation of the modified cryptographic Vernam cipher and the Caesar cipher

2021 ◽  
pp. 62-67
Author(s):  
Valentyna Fastovets
Keyword(s):  
Programming Languages ◽  
Experimental Studies ◽  
Public Key Cryptography ◽  
Software Implementations ◽  
Specialized Education ◽  
Encryption Decryption ◽  
Almost All ◽  
Education Of Students ◽  
Symmetric Key Cryptography ◽  
Modern Cryptography

Problem. Modern cryptography is a very important part of cybersecurity and confidentiality of many operations. It covers almost all parts of our lives, from e-commerce to specialized education of students. Goal. The advantage of symmetric key cryptography is that working with this method is very easy for users, as one key is used for encryption, as well as for decryption purposes, and this key must be secret and should be known only to the sender and recipient and no one else. On the other hand, public key cryptography has two keys. Unfortunately, this exposes the inherent security flaws, as the integrity of the encryption depends entirely on the password. It was decided to consider the implementation of a modified symmetric Vernam cipher that avoids these problems, and its modification and experimental studies should further strengthen data protection. Methodology. Higher mathematics, linear algebra are very important subjects. But if we want to encourage students with cryptography, we need to use all aspects of the IT cluster more effectively. Ideal for this is the implementation of algorithms and programs using programming languages. It is very important and useful for students studying Cybersecurity to illustrate where and how it is possible to create software implementations of encryption / decryption methods. Results. The article presents the analysis and implementation of the modified cryptographic Vernam cipher and Caesar cipher using a concept that combines modern programming languages and the principles of cryptography, which students study in subject-oriented specialties. Originality. An original approach to teaching Cybersecurity students by implementing ciphers using applied programming is described. Practical value. Using cryptography as a learning tool will help students develop their programming skills and effectively understand the concept of cybersecurity in real-world examples.

Automatic Feature Isolation in Network Protocol Software Implementations

2020 ◽  
Author(s):  
Ze Zhang ◽  
Qingzhao Zhang ◽  
Brandon Nguyen ◽  
Sanjay Sri Vallabh Singapuram ◽  
Z. Morley Mao ◽  
...  
Keyword(s):  
Network Protocol ◽  
Software Implementations

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

scholarly journals The Role of Network Science in Glioblastoma

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1045
Author(s):  
Marta B. Lopes ◽  
Eduarda P. Martins ◽  
Susana Vinga ◽  
Bruno M. Costa
Keyword(s):  
Personalized Medicine ◽  
Drug Development ◽  
Information Flow ◽  
Clinical Studies ◽  
Network Science ◽  
Cancer Genomics ◽  
High Dimensional ◽  
Network Discovery ◽  
Software Implementations

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

scholarly journals An encryption–decryption framework to validating single-particle imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Shen ◽  
Colin Zhi Wei Teo ◽  
Kartik Ayyer ◽  
N. Duane Loh
Keyword(s):  
Conceptual Framework ◽  
Single Particle ◽  
Latent Variables ◽  
Ground Truth ◽  
Resolving Power ◽  
Validation Measure ◽  
Encryption Decryption ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

AbstractWe propose an encryption–decryption framework for validating diffraction intensity volumes reconstructed using single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) when the ground truth volume is absent. This conceptual framework exploits each reconstructed volumes’ ability to decipher latent variables (e.g. orientations) of unseen sentinel diffraction patterns. Using this framework, we quantify novel measures of orientation disconcurrence, inconsistency, and disagreement between the decryptions by two independently reconstructed volumes. We also study how these measures can be used to define data sufficiency and its relation to spatial resolution, and the practical consequences of focusing XFEL pulses to smaller foci. This conceptual framework overcomes critical ambiguities in using Fourier Shell Correlation (FSC) as a validation measure for SPI. Finally, we show how this encryption-decryption framework naturally leads to an information-theoretic reformulation of the resolving power of XFEL-SPI, which we hope will lead to principled frameworks for experiment and instrument design.

scholarly journals BESTIE: Broadcast Encryption Scheme for Tiny IoT Equipment

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1389
Author(s):  
Jiwon Lee ◽  
Jihye Kim ◽  
Hyunok Oh
Keyword(s):  
The Other ◽  
Public Key ◽  
Broadcast Encryption ◽  
Encryption Scheme ◽  
The Public ◽  
Private Key ◽  
Diffie Hellman ◽  
The Standard Model ◽  
Encryption Decryption ◽  
Subset Difference

In public key broadcast encryption, anyone can securely transmit a message to a group of receivers such that privileged users can decrypt it. The three important parameters of the broadcast encryption scheme are the length of the ciphertext, the size of private/public key, and the performance of encryption/decryption. It is suggested to decrease them as much as possible; however, it turns out that decreasing one increases the other in most schemes. This paper proposes a new broadcast encryption scheme for tiny Internet of Things (IoT) equipment (BESTIE), minimizing the private key size in each user. In the proposed scheme, the private key size is O(logn), the public key size is O(logn), the encryption time per subset is O(logn), the decryption time is O(logn), and the ciphertext text size is O(r), where n denotes the maximum number of users, and r indicates the number of revoked users. The proposed scheme is the first subset difference-based broadcast encryption scheme to reduce the private key size O(logn) without sacrificing the other parameters. We prove that our proposed scheme is secure under q-Simplified Multi-Exponent Bilinear Diffie-Hellman (q-SMEBDH) in the standard model.

scholarly journals A survey of computational methods in transcriptome-wide alternative splicing analysis

2015 ◽  
Vol 6 (1) ◽  
pp. 59-66 ◽  
Author(s):  
Jianbo Wang ◽  
Zhenqing Ye ◽  
Tim H.-M. Huang ◽  
Huidong Shi ◽  
Victor Jin
Keyword(s):  
Alternative Splicing ◽  
Experimental Validation ◽  
Gene Diversity ◽  
Exon Skipping ◽  
Differential Splicing ◽  
Sequencing Data ◽  
Future Directions ◽  
Specific Analysis ◽  
Software Implementations ◽  
Expected Outcomes

AbstractAlternative splicing is widely recognized for its roles in regulating genes and creating gene diversity. Consequently the identification and quantification of differentially spliced transcripts is pivotal for transcriptome analysis. Here, we review the currently available computational approaches for the analysis of RNA-sequencing data with a focus on exon-skipping events of alternative splicing and discuss the novelties as well as challenges faced to perform differential splicing analyses. In accordance with operational needs we have classified the software tools, which may be instrumental for a specific analysis based on the experimental objectives and expected outcomes. In addition, we also propose a framework for future directions by pinpointing more extensive experimental validation to assess the accuracy of the software predictions and improvements that would facilitate visualizations, data processing, and downstream analyses along with their associated software implementations.

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