Joint Encryption Model Based on a Randomized Autoencoder Neural Network and Coupled Chaos Mapping.

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Anqi HuSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Nanan District, Chongqing 400065, China.ORCID 0000-0001-7162-7693

Xiaoxue GongSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Nanan District, Chongqing 400065, China.ORCID 0000-0002-7440-4003

Lei GuoSchool of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, No. 2, Chongwen Road, Nanan District, Chongqing 400065, China.ORCID 0000-0001-6814-1801

Funding

China Postdoctoral Science Foundation 2021M700563Chongqing Municipal Education Commission CXQT21019, KJQN202100643Chongqing Postdoctoral Funding Project 2112012727685993National Nature Science Foundation of Chin 62025105, 62071076

6 · The paper itself

Abstract

Following an in-depth analysis of one-dimensional chaos, a randomized selective autoencoder neural network (AENN), and coupled chaotic mapping are proposed to address the short period and low complexity of one-dimensional chaos. An improved method is proposed for synchronizing keys during the transmission of one-time pad encryption, which can greatly reduce the usage of channel resources. Then, a joint encryption model based on randomized AENN and a new chaotic coupling mapping is proposed. The performance analysis concludes that the encryption model possesses a huge key space and high sensitivity, and achieves the effect of one-time pad encryption. Experimental results show that this model is a high-security joint encryption model that saves secure channel resources and has the ability to resist common attacks, such as exhaustive attacks, selective plaintext attacks, and statistical attacks.

Indexed as

AENN randomizationcoupled chaos mappingdata encryptionjoint encryptionone-time pad

Identifiers

PMID37628183

DOI10.3390/e25081153

PMCPMC10453204

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.