Evidence map›Paper›PMID 35327895›Full record

ArticleEntropy (Basel, Switzerland)2022

A Novel and Fast Encryption System Based on Improved Josephus Scrambling and Chaotic Mapping.

Zhaoxiong Guan, Junxian Li, Linqing Huang, Xiaoming Xiong, Yuan Liu, Shuting Cai

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

6 authors.

Zhaoxiong GuanSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0001-8862-7788
Junxian LiSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Linqing HuangSchool of Advanced Manufacturing, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0001-9636-499X
Xiaoming XiongSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.
Yuan LiuSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0001-7111-1494
Shuting CaiSchool of Automation, Guangdong University of Technology, Guangzhou 510006, China.ORCID 0000-0002-2842-6439

Funding

National Natural Science Foundation of China 61801127Science and Technology Planning Project of Guangdong Province No.2019B010140002, No.2020B111110002, No.2017B010124003, No.2017B090909001, No.2017B090909004
6 · The paper itself

Abstract

To address the shortcomings of weak confusion and high time complexity of the existing permutation algorithms, including the traditional Josephus ring permutation (TJRP), an improved Josephus ring-based permutation (IJRBP) algorithm is developed. The proposed IJRBP replaces the remove operation used in TJRP with the position exchange operation and employs random permutation steps instead of fixed steps, which can offer a better scrambling effect and a higher permutation efficiency, compared with various scrambling methods. Then, a new encryption algorithm based on the IJRBP and chaotic system is developed. In our scheme, the plaintext feature parameter, which is related to the plaintext and a random sequence generated by a chaotic system, is used as the shift step of the circular shift operation to generate the diffusion matrix, which means that a minor change in the source image will generate a totally different encrypted image. Such a strategy strikes a balance between plaintext sensitivity and ciphertext sensitivity to obtain the ability to resist chosen-plaintext attacks (CPAs) and the high robustness of resisting noise attacks and data loss. Simulation results demonstrate that the proposed image cryptosystem has the advantages of great encryption efficiency and the ability to resist various common attacks.

Indexed as

chaotic mappingefficiencyimage cryptosystemJosephus ringplaintext-relatedscrambling framework

Identifiers

PMID35327895
PMCPMC8947270

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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.