Research Repository

Chaos-based robust method of zero-watermarking for medical signals

Ali, Zulfiqar and Imran, Muhammad and Alsulaiman, Mansour and Shoaib, Muhammad and Ullah, Sana (2018) 'Chaos-based robust method of zero-watermarking for medical signals.' Future Generation Computer Systems, 88. 400 - 412. ISSN 0167-739X

[img]
Preview
Text
Chaos_Accepted.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (788kB) | Preview

Abstract

The growing use of wireless health data transmission via Internet of Things is significantly beneficial to the healthcare industry for optimal usage of health-related facilities. However, at the same time, the use raises concern of privacy protection. Health-related data are private and should be suitably protected. Several pathologies, such as vocal fold disorders, indicate high risks of prevalence in individuals with voice-related occupations, such as teachers, singers, and lawyers. Approximately, one-third of the world population suffers from the voice-related problems during the life span and unauthorized access to their data can create unavoidable circumstances in their personal and professional lives. In this study, a zero-watermarking method is proposed and implemented to protect the identity of patients who suffer from vocal fold disorders. In the proposed method, an image for a patient's identity is generated and inserted into secret keys instead of a host medical signal. Consequently, imperceptibility is naturally achieved. The locations for the insertion of the watermark are determined by a computation of local binary patterns from the time–frequency spectrum. The spectrum is calculated for low frequencies such that it may not be affected by noise attacks. The experimental results suggest that the proposed method has good performance and robustness against noise, and it is reliable in the recovery of an individual's identity.

Item Type: Article
Additional Information: Vocal fold disorders
Uncontrolled Keywords: Chaotic system, Logistic map, Healthcare, Privacy protection
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 09 Apr 2020 11:04
Last Modified: 09 Apr 2020 11:04
URI: http://repository.essex.ac.uk/id/eprint/27203

Actions (login required)

View Item View Item