A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

被引:6
|
作者
Yang, Wencheng [1 ]
Wang, Song [2 ]
Cui, Hui [3 ]
Tang, Zhaohui [1 ]
Li, Yan [1 ]
机构
[1] Univ Southern Queensland, Sch Math Phys & Comp, Toowoomba, Qld 4350, Australia
[2] La Trobe Univ, Sch Comp Engn & Math Sci, Bundoora, Vic 3086, Australia
[3] Monash Univ, Fac IT, Claytyon Campus, Clayton, Vic 3800, Australia
关键词
biometrics; biometric security; privacy; homomorphic encryption; privacy preserving; AUTHENTICATION SYSTEM; SECURITY; INTERNET;
D O I
10.3390/s23073566
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Privacy-preserving biometrics authentication systems using fully homomorphic encryption
    Torres, Wilson Abel Alberto
    Bhattacharjee, Nandita
    Srinivasan, Bala
    [J]. INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS, 2015, 11 (02) : 151 - 168
  • [2] Privacy-Preserving Collective Learning With Homomorphic Encryption
    Paul, Jestine
    Annamalai, Meenatchi Sundaram Muthu Selva
    Ming, William
    Al Badawi, Ahmad
    Veeravalli, Bharadwaj
    Aung, Khin Mi Mi
    [J]. IEEE ACCESS, 2021, 9 : 132084 - 132096
  • [3] A privacy-preserving parallel and homomorphic encryption scheme
    Min, Zhaoe
    Yang, Geng
    Shi, Jingqi
    [J]. OPEN PHYSICS, 2017, 15 (01): : 135 - 142
  • [4] Privacy-Preserving Swarm Learning Based on Homomorphic Encryption
    Chen, Lijie
    Fu, Shaojing
    Lin, Liu
    Luo, Yuchuan
    Zhao, Wentao
    [J]. ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT III, 2022, 13157 : 509 - 523
  • [5] Privacy-Preserving Decentralized Optimization Using Homomorphic Encryption
    Huo, Xiang
    Liu, Mingxi
    [J]. IFAC PAPERSONLINE, 2020, 53 (05): : 630 - 633
  • [6] Privacy-Preserving Federated Learning Using Homomorphic Encryption
    Park, Jaehyoung
    Lim, Hyuk
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (02):
  • [7] On Fully Homomorphic Encryption for Privacy-Preserving Deep Learning
    Hernandez Marcano, Nestor J.
    Moller, Mads
    Hansen, Soren
    Jacobsen, Rune Hylsberg
    [J]. 2019 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2019,
  • [8] Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
    Ono, Shinji
    Takata, Jun
    Kataoka, Masaharu
    Tomohiro, I
    Shin, Kilho
    Sakamoto, Hiroshi
    [J]. ALGORITHMS, 2022, 15 (07)
  • [9] Efficient homomorphic encryption framework for privacy-preserving regression
    Byun, Junyoung
    Park, Saerom
    Choi, Yujin
    Lee, Jaewook
    [J]. APPLIED INTELLIGENCE, 2023, 53 (09) : 10114 - 10129
  • [10] Privacy-preserving genotype imputation with fully homomorphic encryption
    Gursoy, Gamze
    Chielle, Eduardo
    Brannon, Charlotte M.
    Maniatakos, Michail
    Gerstein, Mark
    [J]. CELL SYSTEMS, 2022, 13 (02) : 173 - +