Cargando…
A Review of Homomorphic Encryption for Privacy-Preserving Biometrics
The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biomet...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098691/ https://www.ncbi.nlm.nih.gov/pubmed/37050626 http://dx.doi.org/10.3390/s23073566 |
_version_ | 1785024870966362112 |
---|---|
author | Yang, Wencheng Wang, Song Cui, Hui Tang, Zhaohui Li, Yan |
author_facet | Yang, Wencheng Wang, Song Cui, Hui Tang, Zhaohui Li, Yan |
author_sort | Yang, Wencheng |
collection | PubMed |
description | The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward. |
format | Online Article Text |
id | pubmed-10098691 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100986912023-04-14 A Review of Homomorphic Encryption for Privacy-Preserving Biometrics Yang, Wencheng Wang, Song Cui, Hui Tang, Zhaohui Li, Yan Sensors (Basel) Review The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward. MDPI 2023-03-29 /pmc/articles/PMC10098691/ /pubmed/37050626 http://dx.doi.org/10.3390/s23073566 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Yang, Wencheng Wang, Song Cui, Hui Tang, Zhaohui Li, Yan A Review of Homomorphic Encryption for Privacy-Preserving Biometrics |
title | A Review of Homomorphic Encryption for Privacy-Preserving Biometrics |
title_full | A Review of Homomorphic Encryption for Privacy-Preserving Biometrics |
title_fullStr | A Review of Homomorphic Encryption for Privacy-Preserving Biometrics |
title_full_unstemmed | A Review of Homomorphic Encryption for Privacy-Preserving Biometrics |
title_short | A Review of Homomorphic Encryption for Privacy-Preserving Biometrics |
title_sort | review of homomorphic encryption for privacy-preserving biometrics |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098691/ https://www.ncbi.nlm.nih.gov/pubmed/37050626 http://dx.doi.org/10.3390/s23073566 |
work_keys_str_mv | AT yangwencheng areviewofhomomorphicencryptionforprivacypreservingbiometrics AT wangsong areviewofhomomorphicencryptionforprivacypreservingbiometrics AT cuihui areviewofhomomorphicencryptionforprivacypreservingbiometrics AT tangzhaohui areviewofhomomorphicencryptionforprivacypreservingbiometrics AT liyan areviewofhomomorphicencryptionforprivacypreservingbiometrics AT yangwencheng reviewofhomomorphicencryptionforprivacypreservingbiometrics AT wangsong reviewofhomomorphicencryptionforprivacypreservingbiometrics AT cuihui reviewofhomomorphicencryptionforprivacypreservingbiometrics AT tangzhaohui reviewofhomomorphicencryptionforprivacypreservingbiometrics AT liyan reviewofhomomorphicencryptionforprivacypreservingbiometrics |