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Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

Muhammad Ahmad Nawaz Ul Ghani1, Kun She1,*, Muhammad Arslan Rauf1, Shumaila Khan2, Javed Ali Khan3, Eman Abdullah Aldakheel4, Doaa Sami Khafaga4

1 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
2 Department of Computer Science, University of Science & Technology, Bannu, 28100, Pakistan
3 Department of Computer Science, University of Hertfordshire, Hatfield, AL10 9AB, UK
4 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia

* Corresponding Author: Kun She. Email: email

(This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Frameworks for Signal and Image Processing Applications)

Computers, Materials & Continua 2024, 79(2), 2609-2623. https://doi.org/10.32604/cmc.2024.049611

Abstract

The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing to solve privacy concerns while maintaining exact face recognition. The framework’s painstaking design and execution strive to strike a compromise between precise face recognition and protecting personal data integrity in an increasingly interconnected environment. Using cutting-edge tools like Dlib for face analysis, Ray Cluster for distributed computing, and Blockchain for decentralized identity verification, the proposed system provides scalable and secure facial analysis while protecting user privacy. The study’s contributions include the creation of a sustainable and scalable solution for privacy-aware face recognition, the implementation of flexible privacy computing approaches based on Blockchain networks, and the demonstration of higher performance over previous methods. Specifically, the proposed StyleGAN model has an outstanding accuracy rate of 93.84% while processing high-resolution images from the CelebA-HQ dataset, beating other evaluated models such as Progressive GAN 90.27%, CycleGAN 89.80%, and MGAN 80.80%. With improvements in accuracy, speed, and privacy protection, the framework has great promise for practical use in a variety of fields that need face recognition technology. This study paves the way for future research in privacy-enhanced face recognition systems, emphasizing the significance of using cutting-edge technology to meet rising privacy issues in digital identity.

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APA Style
Ghani, M.A.N.U., She, K., Rauf, M.A., Khan, S., Khan, J.A. et al. (2024). Enhancing security and privacy in distributed face recognition systems through blockchain and GAN technologies. Computers, Materials & Continua, 79(2), 2609-2623. https://doi.org/10.32604/cmc.2024.049611
Vancouver Style
Ghani MANU, She K, Rauf MA, Khan S, Khan JA, Aldakheel EA, et al. Enhancing security and privacy in distributed face recognition systems through blockchain and GAN technologies. Comput Mater Contin. 2024;79(2):2609-2623 https://doi.org/10.32604/cmc.2024.049611
IEEE Style
M.A.N.U. Ghani et al., "Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies," Comput. Mater. Contin., vol. 79, no. 2, pp. 2609-2623. 2024. https://doi.org/10.32604/cmc.2024.049611



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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