Ali, Zulfiqar and Imran, Muhammad and McClean, Sally and Khan, Naveed and Shoaib, Muhammad (2019) Protection of records and data authentication based on secret shares and watermarking. Future Generation Computer Systems, 98. pp. 331-341. DOI https://doi.org/10.1016/j.future.2019.01.050
Ali, Zulfiqar and Imran, Muhammad and McClean, Sally and Khan, Naveed and Shoaib, Muhammad (2019) Protection of records and data authentication based on secret shares and watermarking. Future Generation Computer Systems, 98. pp. 331-341. DOI https://doi.org/10.1016/j.future.2019.01.050
Ali, Zulfiqar and Imran, Muhammad and McClean, Sally and Khan, Naveed and Shoaib, Muhammad (2019) Protection of records and data authentication based on secret shares and watermarking. Future Generation Computer Systems, 98. pp. 331-341. DOI https://doi.org/10.1016/j.future.2019.01.050
Abstract
The rapid growth in communication technology facilitates the health industry in many aspects from transmission of sensor’s data to real-time diagnosis using cloud-based frameworks. However, the secure transmission of data and its authenticity become a challenging task, especially, for health-related applications. The medical information must be accessible to only the relevant healthcare staff to avoid any unfortunate circumstances for the patient as well as for the healthcare providers. Therefore, a method to protect the identity of a patient and authentication of transmitted data is proposed in this study. The proposed method provides dual protection. First, it encrypts the identity using Shamir’s secret sharing scheme without the increase in dimension of the original identity. Second, the identity is watermarked using zero-watermarking to avoid any distortion into the host signal. The experimental results show that the proposed method encrypts, embeds and extracts identities reliably. Moreover, in case of malicious attack, the method distorts the embedded identity which provides a clear indication of fabrication. An automatic disorder detection system using Mel-frequency cepstral coefficients and Gaussian mixture model is also implemented which concludes that malicious attacks greatly impact on the accurate diagnosis of disorders.
Item Type: | Article |
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Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 06 Aug 2021 11:59 |
Last Modified: | 30 Oct 2024 16:37 |
URI: | http://repository.essex.ac.uk/id/eprint/27200 |
Available files
Filename: FGCS_authentication_final_v9.pdf
Licence: Creative Commons: Attribution-Noncommercial-No Derivative Works 3.0