Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning

Muhammad Ahmad Nawaz Ul Ghani; Kun She; Muhammad Arslan Rauf; Shumaila Khan; Masoud Alajmi; Yazeed Yasin Ghadi; Hend Khalid Alkahtani; Muhammad Ahmad Nawaz Ul Ghani; Kun She; Muhammad Arslan Rauf; Shumaila Khan; Masoud Alajmi; Yazeed Yasin Ghadi; Hend Khalid Alkahtani

doi:10.3934/mbe.2024184

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 3: 4165-4186. doi: 10.3934/mbe.2024184

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Research article Special Issues

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning

1.
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
2.
Department of Computer Science, University of Science & Technology Bannu, Bannu, Pakistan
3.
Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
4.
Department of Computer Science, Al Ain University, UAE
5.
Department of Information Systems, College of Computer and Information Sciences Princess Nourah bint Abdulrahman University, 11671, Riyadh, Saudi Arabia

Academic Editor: Yang Kuang

Received: 30 December 2023 Revised: 24 January 2024 Accepted: 04 February 2024 Published: 26 February 2024

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.
- facial recognition,
- data security,
- blockchain,
- privacy protection,
- decentralized system
Citation: Muhammad Ahmad Nawaz Ul Ghani, Kun She, Muhammad Arslan Rauf, Shumaila Khan, Masoud Alajmi, Yazeed Yasin Ghadi, Hend Khalid Alkahtani. Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning[J]. Mathematical Biosciences and Engineering, 2024, 21(3): 4165-4186. doi: 10.3934/mbe.2024184

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Abstract

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.

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