Show simple item record

dc.contributor.authorGhani, Muhammad Ahmad Nawaz Ul
dc.contributor.authorShe, Kun
dc.contributor.authorRauf, Muhammad Arslan
dc.contributor.authorKhan, Shumaila
dc.contributor.authorKhan, Javed Ali
dc.contributor.authorAldakheel, Eman Abdullah
dc.contributor.authorKhafaga, Doaa Sami
dc.date.accessioned2024-05-31T15:18:59Z
dc.date.available2024-05-31T15:18:59Z
dc.date.issued2024-05-15
dc.identifier.citationGhani , M A N U , She , K , Rauf , M A , Khan , S , Khan , J A , Aldakheel , E A & Khafaga , D S 2024 , ' Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies ' , Computers, Materials & Continua , vol. 79 , no. 2 , 049611 , pp. 2609-2623 . https://doi.org/10.32604/cmc.2024.049611 , https://doi.org/10.32604/cmc.2024.049611
dc.identifier.issn1546-2218
dc.identifier.otherBibtex: cmc.2024.049611
dc.identifier.otherORCID: /0000-0003-3306-1195/work/160699873
dc.identifier.urihttp://hdl.handle.net/2299/27930
dc.descriptionThis is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/
dc.description.abstractThe 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.en
dc.format.extent15
dc.format.extent1643955
dc.language.isoeng
dc.relation.ispartofComputers, Materials & Continua
dc.subjectblockchain
dc.subjectdistributed systems
dc.subjectFacial recognition
dc.subjectGAN
dc.subjectprivacy protection
dc.subjectBiomaterials
dc.subjectModelling and Simulation
dc.subjectMechanics of Materials
dc.subjectComputer Science Applications
dc.subjectElectrical and Electronic Engineering
dc.titleEnhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologiesen
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionCybersecurity and Computing Systems
dc.contributor.institutionBiocomputation Research Group
dc.contributor.institutionDepartment of Computer Science
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85193079488&partnerID=8YFLogxK
rioxxterms.versionofrecord10.32604/cmc.2024.049611
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record