Securing Text Messages Using Graph Theory and Steganography

Abdul-Ghani, Samaher Adnan; Abdul-Wahhab, Renna D.; Abood, Enas Wahab

doi:10.21123/bsj.2022.19.1.0189

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…Hence, after deleting 𝑒 1 and 𝑒 2 from 𝐺𝑃(𝑛, 1), we get the graph in Figure (2). Next, 𝑑 presents the diameter of the connected graphs 𝐺𝑝(𝑛, 1,2) obtained upon removing 𝑡 = 2 edges from 𝐺𝑝(𝑛, 1).…”

Section: Case 1: 𝒕 =mentioning

confidence: 99%

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Solving Edges Deletion Problem of Generalized Petersen Graphs

Nasif Jasim,

Najim

2024

Jour. Kufa Math. Comp.

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The dependability and effectiveness of a network can be investigated using a variety of graph-theoretic techniques, and the network's connectivity determines how reliable it is. Diameter in a network is often used to measure the efficiency of a network in the event that a network experiences issues such as a decline in the communication signal or a breakdown in the communication between its components. This paper examines the increase in diameter of the generalized Petersen graph after removing a certain number of edges. Finding the exact values of f(n,t) that represent the maximum diameter of an altered generalized Petersen graph GP(n,k,t) obtained after removing edges from for k=1 and t≥2.

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Section: Case 1: 𝒕 =mentioning

confidence: 99%

“…Systems for data transmission and communication must include data security. Its primary responsibility is to safeguard and integrate sensitive data from the source to the recipient [2].…”

Section: Introductionmentioning

confidence: 99%

Solving Edges Deletion Problem of Generalized Petersen Graphs

Nasif Jasim,

Najim

2024

Jour. Kufa Math. Comp.

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“…To evaluate the recovery capability of the proposed system, the normalized mean square error (MSE) and peak signal-to-noise ratio metrics (PSNR) parameters were used [33]. These parameters were computed between the original and reconstructed signals, given by:…”

Section: Psnr and Mse Analysismentioning

confidence: 99%

Provably secure and efficient audio compression based on compressive sensing

Abood

Hussien²,

Kawi³

et al. 2023

IJECE

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<span lang="EN-US">The advancement of systems with the capacity to compress audio signals and simultaneously secure is a highly attractive research subject. This is because of the need to enhance storage usage and speed up the transmission of data, as well as securing the transmission of sensitive signals over limited and insecure communication channels. Thus, many researchers have studied and produced different systems, either to compress or encrypt audio data using different algorithms and methods, all of which suffer from certain issues including high time consumption or complex calculations. This paper proposes a compressing sensing-based system that compresses audio signals and simultaneously provides an encryption system. The audio signal is segmented into small matrices of samples and then multiplied by a non-square sensing matrix generated by a Gaussian random generator. The reconstruction process is carried out by solving a linear system using the pseudoinverse of Moore-Penrose. The statistical analysis results obtaining from implementing different types and sizes of audio signals prove that the proposed system succeeds in compressing the audio signals with a ratio reaching 28% of real size and reconstructing the signal with a correlation metric between 0.98 and 0.99. It also scores very good results in the normalized mean square error (MSE), peak signal-to-noise ratio metrics (PSNR), and the structural similarity index (SSIM), as well as giving the signal a high level of security.</span>

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“…Numerous studies have been conducted on various families of graphs using various fortunate labelling techniques, including Lucky Edge Lableing 3,4 , Proper Lucky Labeling 5 , e Lucky Labeling 6 , d-Lucky Labeling 7 , Proper d-Lucky Labeling 8 etc. This paper aims to find the lucky number of prominent families [9][10][11] namely Complete graphs and Complete Bipartite graphs.…”

Section: Introductionmentioning

confidence: 99%

Some New Results on Lucky Labeling

Ashwini

Selvam²,

Gnanajothi³

2023

Baghdad Sci.J

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Czerwi’nski et al. introduced Lucky labeling in 2009 and Akbari et al and A.Nellai Murugan et al studied it further. Czerwi’nski defined Lucky Number of graph as follows: A labeling of vertices of a graph G is called a Lucky labeling if for every pair of adjacent vertices u and v in G where . A graph G may admit any number of lucky labelings. The least integer k for which a graph G has a lucky labeling from the set 1, 2, k is the lucky number of G denoted by η(G). This paper aims to determine the lucky number of Complete graph Kn, Complete bipartite graph Km,n and Complete tripartite graph Kl,m,n. It has also been studied how the lucky number changes while adding a graph G with Kn and deleting an edge e from Kn.

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Securing Text Messages Using Graph Theory and Steganography

Cited by 7 publications

References 15 publications

Solving Edges Deletion Problem of Generalized Petersen Graphs

Solving Edges Deletion Problem of Generalized Petersen Graphs

Provably secure and efficient audio compression based on compressive sensing

Some New Results on Lucky Labeling

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