Evidence map›Paper›PMID 39330093›Full record

ArticleEntropy (Basel, Switzerland)2024

A New Diffusion Strategy Using an Epidemic Spreading Model for Encryption.

Wei Zhang, Guangdong Zhu, Meng Xing, Jingjing Yang, Hai Yu, Zhiliang Zhu

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2024. 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

6 authors.

Wei ZhangCollege of Software, Northeastern University, Shenyang 110167, China.
Guangdong ZhuCollege of Software, Northeastern University, Shenyang 110167, China.
Meng XingCollege of Software, Northeastern University, Shenyang 110167, China.
Jingjing YangCollege of Software, Northeastern University, Shenyang 110167, China.
Hai YuCollege of Software, Northeastern University, Shenyang 110167, China.
Zhiliang ZhuCollege of Software, Northeastern University, Shenyang 110167, China.

Funding

National Natural Science Foundation of China 61902056National Natural Science Foundation of China 61977014Natural Science Foundation of Liaoning Province of China 2022-MS-123
6 · The paper itself

Abstract

The diffusion phenomenon that exhibits intrinsic similarities is pervasive in cryptography and natural systems, evident in liquid diffusion, epidemic spread, animal migration, and encryption techniques. In cryptography, bytes are systematically diffused in a sequential manner to encrypt the value of each byte in the plaintext in a linear fashion. In contrast, within an epidemic spreading model, the diffusion process can be represented within a complex, multilayered network, encompassing layers such as familial and social transmission dynamics. Transmission links establish connections both within and between individual layers. It has had a more rapid spread than linear approaches due to the particularization of non-linear transmission. In this study, the novelty of a cryptography diffusion strategy based on an epidemic model is first proposed, in which pixels and their dynamic adjacency are considered as vertices and edges, respectively, within a complex network framework. Subsequently, the encryption process is governed by the Susceptible-Vaccinated-Infected-Recovered (SVIR) model integrated with chaotic dynamics. Simulation results demonstrate that the proposed algorithm exhibits faster encryption speed while effectively resisting brute force, statistical, and differential attacks. Furthermore, it demonstrates strong robustness against noise interference and data loss.

Indexed as

chaosencryptionepidemic spreadingSVIR model

Identifiers

PMID39330093
PMCPMC11431050

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.