Chenu, Sai Teja (2024) Biometric based Fingerprint Verification System using Deep Learning for Secure Cloud Storage. Masters thesis, Dublin, National College of Ireland.
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Abstract
This project provides the fingerprint identification system which is accurate for this type of biometric as well by using SNN to increase rate of Biometric Recognition. This is the primary structure of proposed system SNNs for improved accuracy matching Fingerprint to provide strength security cloud distribution systems. This solution personalized an intuitive customer biometric approach interoperable with the cloud in order to follow best-in-class data privacy and security principals. Protecting not only biometric data in the cloud, as well as any other information uploaded to public servers anywhere it is the Holy Grail of cloud security. This might sound quite a bit over-specified, but the results were pretty good: it answers correctly with 99.67% on Robust. Classification report: accuracy and F1-score 100%. Therefore, this system is believed to be an innovative new technique in the secure data publication for guaranteeing protection of individual and organizational information released below cloud integration.
Item Type: | Thesis (Masters) |
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Supervisors: | Name Email Monaghan, Mark UNSPECIFIED |
Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Biometric Identification T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security |
Divisions: | School of Computing > Master of Science in Cyber Security |
Depositing User: | Ciara O'Brien |
Date Deposited: | 29 Jul 2025 11:23 |
Last Modified: | 29 Jul 2025 11:23 |
URI: | https://norma.ncirl.ie/id/eprint/8301 |
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