Multi-Party Verifiably Collaborative Encryption for Biomedical Signals via Singular Spectrum Analysis-Based Chaotic Filter Bank Networks
Xiwen Zhang, Jianfeng He, Bingo Wing-Kuen Ling
TL;DR
This paper introduces a new encryption system for biomedical signals using chaotic networks and singular spectrum analysis, allowing multiple parties to collaborate securely.
Contribution
A novel multi-party encryption system using SSA-based chaotic networks for biomedical signals is proposed.
Findings
The proposed system effectively encrypts nonlinear and non-stationary biomedical signals.
The encryption performance was validated through computer simulations.
The system parameters adapt to the number of collaborating parties.
Abstract
This paper proposes a multi-party verifiably collaborative system for encrypting the nonlinear and the non-stationary biomedical signals captured by biomedical sensors via the singular spectrum analysis (SSA)-based chaotic networks. In particular, the raw signals are first decomposed into the multiple components by the SSA. Then, these decomposed components are fed into the chaotic filter bank networks for performing the encryption. To perform the multi-party verifiably collaborative encryption, the window length of the SSA and the total number of the layers in the chaotic network are flexibly designed to match the total number of the collaborators. The computer numerical simulation results show that our proposed system achieves a good encryption performance.
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Chaos control and synchronization · Mathematical Analysis and Transform Methods