Author | Mohammadi, Maryam |
Author | Sabry, Farida |
Author | Labda, Wadha |
Author | Malluhi, Qutaibah |
Available date | 2024-07-17T07:14:39Z |
Publication Date | 2023 |
Publication Name | IWSPA 2023 - Proceedings of the 9th ACM International Workshop on Security and Privacy Analytics |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1145/3579987.3586568 |
URI | http://hdl.handle.net/10576/56733 |
Abstract | Fingerprint recognition is a widely adopted biometric authentication method that leverages the unique characteristics of fingerprints to identify individuals. Its applications range from access control and authentication to forensic science, making the development of a robust, precise, and secure model for fingerprint recognition and analysis of paramount importance. Recently, deep learning and machine learning models have shown promise in this field, however, the use of these models raises significant privacy concerns as there is a potential for private fingerprint data to be compromised. This research aims to address these concerns by incorporating differential privacy techniques to protect the privacy of fingerprints. It provides evidence that the use of differential privacy technique leads to acceptable trade-off between preserving the privacy of fingerprints and accuracy of fingerprint recognition systems while maintaining robustness against model inversion attacks. With a noise multiplier of 0.01, the verification model attains a good accuracy of 89.32% at privacy budget ε = 1.2, and less than 80% at lower privacy budget for the identification model. |
Sponsor | This publication was partially funded by a grant from Qatar National Research Fund (QNRF), Project Number ECRA 01-006-1-001 for most of the work done by the second author while working in the project. The contents of this research are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund (QNRF). |
Language | en |
Publisher | Association for Computing Machinery, Inc |
Subject | deep-learning; differential privacy; fingerprint data security; fingerprint recognition; model inversion attack
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Title | Privacy-preserving Deep-learning Models for Fingerprint Data using Differential Privacy |
Type | Conference |
Pagination | 45-53 |
dc.accessType
| Abstract Only |