Indices of regularity and indices of randomness for m-ary strings

Skliar, Osvaldo; Monge, Ricardo E.; Oviedo, Guillermo R.; Medina, Víctor Manuel Pina

doi:10.15517/rmta.v16i1.1418

Cited by 4 publications

(6 citation statements)

References 4 publications

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“…To be certain about the genuine randomness of each RBS used, one must verify that each sequence passes the most rigorous randomness tests available: [10] and [11].…”

Section: Discussion and Prospectsmentioning

confidence: 99%

A New Cryptographic Approach: Iterated Random Encryption (IRE)

Skliar,

Gapper,

Monge

2018

Preprint

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A new cryptographic approach -Iterated Random Encryption (IRE) -is presented here. Although it is very simple, and easy to implement, it provides a very high level of security. According to this approach, a sequence of operations applied to a message (M ) yields the encrypted message (M E ). In that series of operations, the one with the most important role is operation 6, which involves a random binary sequence (RBS) generated by using the Hybrid Random Number Generator (HRNG) or the Mathematical Random Number Generator (MRNG). A sequence of anti-operations applied to M E makes it possible to recover M .

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“…To be certain about the genuine randomness of each RBS used, one must verify that each sequence passes the most rigorous randomness tests available: [10] and [11].…”

Section: Discussion and Prospectsmentioning

confidence: 99%

A New Cryptographic Approach: Iterated Random Encryption (IRE)

Skliar,

Gapper,

Monge

2018

Preprint

Self Cite

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“…A less-known complexity measure, known as the regularity index [21], appears more promising, but will not be covered here. The regularity index is a generic variant of the index of maximum regularity, described as a new tool for computational analysis of DNA [22].…”

Section: Previous Work In Dna Complexity Studymentioning

confidence: 99%

Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction

Monge

Crespo

2015

Entropy

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This study delves further into the analysis of genomic data by computing a variety of complexity measures. We analyze the effect of window size and evaluate the precision and recall of the prediction of gene zones, aided with a much larger dataset (full chromosomes). A technique based on the separation of two cases (gene-containing and non-gene-containing) has been developed as a basic gene predictor for automated DNA analysis. This predictor was tested on various sequences of human DNA obtained from public databases, in a set of three experiments. The first one covers window size and other parameters; the second one corresponds to an analysis of a full human chromosome (198 million nucleic acids); and the last one tests subject variability (with five different individual subjects). All three experiments have high-quality results, in terms of recall and precision, thus indicating the effectiveness of the predictor.

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“…In a previous article [2] we introduced the concept of "index of randomness" for m-ary strings, where m = 2, 3, 4, . .…”

Section: Percentage Of Areas Under Curve Intervalmentioning

confidence: 99%

A Mathematical Random Number Generator (MRNG)

Skliar¹,

Monge²,

Gapper³

et al. 2012

Preprint

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A novel Mathematical Random Number Generator (MRNG) is presented here. In this case, "mathematical" refers to the fact that to construct that generator it is not necessary to resort to a physical phenomenon, such as the thermal noise of an electronic device, but rather to a mathematical procedure. The MRNG generates binary strings -in principle, as long as desired -which may be considered genuinely random in the sense that they pass the statistical tests currently accepted to evaluate the randomness of those strings. From those strings, the MRNG also generates random numbers expressed in base 10. An MRNG has been installed as a facility on the following web page: www.appliedmathgroup.org. This generator may be used for applications in tasks in: a) computational simulation of probabilistic-type systems, and b) the random selection of samples of different populations. Users interested in applications in cryptography can build another MRNG, but they would have to withhold information -specified in section 5 -from people who are not authorized to decode messages encrypted using that resource.

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Indices of regularity and indices of randomness for m-ary strings

Cited by 4 publications

References 4 publications

A New Cryptographic Approach: Iterated Random Encryption (IRE)

A New Cryptographic Approach: Iterated Random Encryption (IRE)

Analysis of Data Complexity in Human DNA for Gene-Containing Zone Prediction

A Mathematical Random Number Generator (MRNG)

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