Neural Synchronization by Mutual Learning Using Genetic Approach for Secure Key Generation

2012 ◽  
pp. 422-431 ◽  
Author(s):  
S. Santhanalakshmi ◽  
T. S. B. Sudarshan ◽  
Gopal K. Patra
Keyword(s):  
Key Generation ◽  
Genetic Approach ◽  
Neural Synchronization ◽  
Mutual Learning

scholarly journals Mutual learning-based efficient synchronization of neural networks to exchange the neural key

2021 ◽  
Author(s):  
Arindam Sarkar
Keyword(s):  
Neural Networks ◽  
Weight Vector ◽  
Key Exchange ◽  
Security And Privacy ◽  
Complete Synchronization ◽  
Neural Synchronization ◽  
Mutual Learning ◽  
Public Channel ◽  
Improved Technique

AbstractSynchronization of two neural networks through mutual learning is used to exchange the key over a public channel. In the absence of a weight vector from another party, the key challenge with neural synchronization is how to assess the coordination of two communication parties. There is an issue of delay in the current techniques in the synchronization assessment that has an impact on the security and privacy of the neural synchronization. In this paper, to assess the complete coordination of a cluster of neural networks more efficiently and timely, an important strategy for assessing coordination is presented. To approximately determine the degree of synchronization, the frequency of the two networks having the same output in prior iterations is used. The hash is used to determine if both the networks are completely synchronized exactly when a certain threshold is crossed. The improved technique makes absolute coordination between two communication parties using the weight vectors’ has value. In contrast, with existing approaches, two communicating parties who follow the proposed approach will detect complete synchronization sooner. This reduces the effective geometric likelihood. The proposed method, therefore, increases the safety of the protocol for neural key exchange. This proposed technique has been passed through different parametric tests. Simulations of the process show effectiveness in terms of cited results in the paper.

Coexistence of Pluralities through Practices of Intercultural Relationships

10.1558/37327 ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. 155-176 ◽  
Author(s):  
Jorge E. Castillo Guerra
Keyword(s):  
Latin American ◽  
Learning Processes ◽  
Intercultural Relationships ◽  
Mutual Learning ◽  
Cultural Orientations ◽  
Intercultural Philosophy

This article investigates how migrants and refugees contribute to forms of co-existence among peoples with different religious and cultural orientations. Drawing on theories of intercultural philosophy and decolonial thinking, the author focuses on transformations of identity and faith among Catholic Latin American migrants in Europe and the United Sates of America. He argues that when these migrants encounter exclusion and uprooting, processes of transformation converge in parish communities. There they create mutual learning processes leading to new intercultural practices such as the deaconry of culture and relationship.

An Efficient Key Generation of ZHFE Public Key Cryptosystem

2018 ◽  
Vol E101.A (1) ◽  
pp. 29-38
Author(s):  
Yasuhiko IKEMATSU ◽  
Dung Hoang DUONG ◽  
Albrecht PETZOLDT ◽  
Tsuyoshi TAKAGI
Keyword(s):  
Public Key ◽  
Public Key Cryptosystem ◽  
Key Generation

`Diet GMKTN55' Offers Accelerated Benchmarking Through a Representative Subset Approach

2018 ◽  
Author(s):  
Tim Gould
Keyword(s):  
Density Functional ◽  
Chem Phys ◽  
Comprehensive Analysis ◽  
Genetic Approach ◽  
Representative Subset ◽  
Phys Chem Chem Phys

The GMTKN55 benchmarking protocol introduced by [Goerigk et al., Phys. Chem. Chem. Phys., 2017, 19, 32184] allows comprehensive analysis and ranking of density functional approximations with diverse chemical behaviours. But this comprehensiveness comes at a cost: GMTKN55's 1500 benchmarking values require energies for around 2500 systems to be calculated, making it a costly exercise. This manuscript introduces three subsets of GMTKN55, consisting of 30, 100 and 150 systems, as `diet' substitutes for the full database. The subsets are chosen via a stochastic genetic approach, and consequently can reproduce key results of the full GMTKN55 database, including ranking of approximations.

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