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

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

PMC · DOI: 10.3390/e26090760 · 2024-09-05

## TL;DR

This paper introduces a new encryption method inspired by how diseases spread, offering faster encryption and better security against attacks.

## Contribution

A novel cryptography diffusion strategy based on an epidemic model and complex networks is proposed.

## Key findings

- The proposed encryption algorithm is faster than traditional linear methods.
- It effectively resists brute force, statistical, and differential attacks.
- The method is robust against noise and data loss.

## 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.

## Full-text entities

- **Diseases:** injury to people or property (MESH:C000719191), COVID-19 (MESH:D000086382), death (MESH:D003643), infected (MESH:D007239), infectious disease (MESH:D003141)
- **Chemicals:** salt-and-pepper (-), S (MESH:D013455), I (MESH:D007455), salt- (MESH:D012492)
- **Species:** Measles morbillivirus (no rank) [taxon 11234]

## Figures

11 figures with captions in the complete paper: https://tomesphere.com/paper/PMC11431050/full.md

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Source: https://tomesphere.com/paper/PMC11431050